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@Article{alvarez_2007,
  Title                    = {{Model based analysis of real-time {PCR} data from {DNA} binding dye protocols}},
  Author                   = {Mariano J. Alvarez and Guillermo J. Vila-Ortiz and Mariano C. Salibe and Osvaldo L. Podhajcer and Fernando J. Pitossi},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2007},

  Month                    = mar,
  Number                   = {1},
  Pages                    = {85},
  Volume                   = {8},

  Abstract                 = {Reverse transcription followed by real-time {PCR} is widely used for quantification of specific {mRNA}, and with the use of double-stranded {DNA} binding dyes it is becoming a standard for microarray data validation. Despite the kinetic information generated by real-time {PCR}, most popular analysis methods assume constant amplification efficiency among samples, introducing strong biases when amplification efficiencies are not the same. {PMID}: 17349040},
  Copyright                = {2007 Alvarez et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-8-85},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {17349040},
  Url                      = {http://www.biomedcentral.com/1471-2105/8/85/abstract},
  Urldate                  = {2014-07-01}
}

@Article{asiello_2011,
  Title                    = {{Miniaturized isothermal nucleic acid amplification, a review}},
  Author                   = {Peter J Asiello and Antje J Baeumner},
  Journal                  = {Lab on a {C}hip},
  Year                     = {2011},

  Month                    = apr,
  Note                     = {{PMID:} 21387067},
  Number                   = {8},
  Pages                    = {1420--1430},
  Volume                   = {11},

  Abstract                 = {Micro-Total Analysis Systems (\mu{TAS}) for use in on-site rapid detection of {DNA} or {RNA} are increasingly being developed. Here, amplification of the target sequence is key to increasing sensitivity, enabling single-cell and few-copy nucleic acid detection. The several advantages to miniaturizing amplification reactions and coupling them with sample preparation and detection on the same chip are well known and include fewer manual steps, preventing contamination, and significantly reducing the volume of expensive reagents. To-date, the majority of miniaturized systems for nucleic acid analysis have used the polymerase chain reaction ({PCR)} for amplification and those systems are covered in previous reviews. This review provides a thorough overview of miniaturized analysis systems using alternatives to {PCR}, specifically isothermal amplification reactions. With no need for thermal cycling, isothermal microsystems can be designed to be simple and low-energy consuming and therefore may outperform {PCR} in portable, battery-operated detection systems in the future. The main isothermal methods as miniaturized systems reviewed here include nucleic acid sequence-based amplification ({NASBA)}, loop-mediated isothermal amplification ({LAMP)}, helicase-dependent amplification ({HDA)}, rolling circle amplification ({RCA)}, and strand displacement amplification ({SDA).} Also, important design criteria for the miniaturized devices are discussed. Finally, the potential of miniaturization of some new isothermal methods such as the exponential amplification reaction ({EXPAR)}, isothermal and chimeric primer-initiated amplification of nucleic acids ({ICANs)}, signal-mediated amplification of {RNA} technology ({SMART)} and others is presented.},
  Doi                      = {10.1039/c0lc00666a},
  ISSN                     = {1473-0189},
  Keywords                 = {Humans; Miniaturization; Nucleic Acid Amplification Techniques; Temperature},
  Language                 = {eng}
}

@Article{boehringer_2013,
  Title                    = {{Dynamic Parallelization of {R} Functions}},
  Author                   = {Stefan B{\"o}hringer},
  Journal                  = {The R Journal},
  Year                     = {2013},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {88--97},
  Volume                   = {5},

  Url                      = {http://journal.r-project.org/archive/2013-2/boehringer.pdf}
}

@Article{Baaaath_2012,
  Title                    = {{The State of Naming Conventions in {R}}},
  Author                   = {Rasmus B{\aa}{\aa}th},
  Journal                  = {{The R Journal}},
  Year                     = {2012},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {74--75},
  Volume                   = {4},

  Url                      = {http://journal.r-project.org/archive/2012-2/RJournal_2012-2_Baaaath.pdf}
}

@Article{batsch_2008,
  Title                    = {{Simultaneous fitting of real-time {PCR} data with efficiency of amplification modeled as Gaussian function of target fluorescence}},
  Author                   = {Anke Batsch and Andrea Noetel and Christian Fork and Anita Urban and Daliborka Lazic and Tina Lucas and Julia Pietsch and Andreas Lazar and Edgar Sch{\"o}mig and Dirk Gr{\"u}ndemann},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2008},

  Month                    = feb,
  Number                   = {1},
  Pages                    = {95},
  Volume                   = {9},

  Abstract                 = {In real-time {PCR}, it is necessary to consider the efficiency of amplification ({EA}) of amplicons in order to determine initial target levels properly. {EAs} can be deduced from standard curves, but these involve extra effort and cost and may yield invalid {EAs}. Alternatively, {EA} can be extracted from individual fluorescence curves. Unfortunately, this is not reliable enough. {PMID}: 18267040},
  Copyright                = {2008 Batsch et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-9-95},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {18267040},
  Url                      = {http://www.biomedcentral.com/1471-2105/9/95/abstract},
  Urldate                  = {2014-07-01}
}

@Manual{blagodatskikh_2014,
  Title                    = {{Importing real-time thermo cycler ({qPCR}) data from {RDML} format files}},
  Author                   = {Konstantin A. Blagodatskikh and Stefan Roediger and Michal Burdukiewicz},
  Month                    = sep,
  Year                     = {2014},

  Url                      = {http://CRAN.R-project.org/package=RDML},
  Urldate                  = {2014-09-13}
}

@Article{Blanton_2014,
  Title                    = {A Scientist’s Perspective on Sustainable Scientific Software},
  Author                   = {Brian Blanton and Chris Lenhardt},
  Journal                  = {Journal of Open Research Software},
  Year                     = {2014},
  Number                   = {1},
  Volume                   = {2},

  Abstract                 = {Software underpins most of our daily activities, from banking and finance to interactions with the internet, to weather forecasts and reports. Software also impacts individuals, groups, and societies through policy implementation, since information for decision and policy making is frequently derived from software ranging from climate and weather models to financial forecasting systems. One way to gauge the extent to which specific software needs to be sustainable, accessible, and transparent essentially hinges on the degree to which scientific analysis software, models, and model output are used to help inform and guide policy. Climate models and related scientific results are perhaps the most obvious example of the need for sustainable and transparent software, due in part to the public forum in which the results are scrutinized and the implications on environmental management policy. Without almost ubiquitous adoption of best practices for scientific software development, maintenance, and use, the credibility of scientific results and of ourselves as scientists is substantially at risk; sustainable and transparent research processes are thus at the heart of maintaining and increasing our collective reputations. [The authors want to make clear that, by using climate models as an example of software with policy impacts, we are not claiming that these models, are written with little “best practices” in mind, nor that they are inherently unsustainable as software.]},
  ISSN                     = {2049-9647},
  Keywords                 = {scientific software; credibility; credibility risk},
  Url                      = {http://openresearchsoftware.metajnl.com/article/view/jors.ba}
}

@Manual{burdukiewicz_2014,
  Title                    = {{{dpcR}: Digital {PCR} Analysis}},
  Author                   = {Michal Burdukiewicz and Stefan Roediger},
  Month                    = jul,
  Year                     = {2014},

  Abstract                 = {Analysis, visualisation and simulation of digital {PCR} experiments.},
  Copyright                = {{GPL}-2},
  Shorttitle               = {{dpcR}},
  Url                      = {http://cran.r-project.org/web/packages/dpcR/index.html},
  Urldate                  = {2014-09-20}
}

@Article{bustin_miqe_2009,
  Title                    = {{The {MIQE} guidelines: minimum information for publication of quantitative real-time {PCR} experiments}},
  Author                   = {Stephen A. Bustin and Vladimir Benes and Jeremy A. Garson and Jan Hellemans and Jim Huggett and Mikael Kubista and Reinhold Mueller and Tania Nolan and Michael W. Pfaffl and Gregory L. Shipley and Jo Vandesompele and Carl T. Wittwer},
  Journal                  = {Clinical Chemistry},
  Year                     = {2009},

  Month                    = apr,
  Number                   = {4},
  Pages                    = {611--622},
  Volume                   = {55},

  Abstract                 = {{BACKGROUND}: Currently, a lack of consensus exists on how best to perform and interpret quantitative real-time {PCR} ({qPCR}) experiments. The problem is exacerbated by a lack of sufficient experimental detail in many publications, which impedes a reader's ability to evaluate critically the quality of the results presented or to repeat the experiments. {CONTENT}: The Minimum Information for Publication of Quantitative Real-Time {PCR} Experiments ({MIQE}) guidelines target the reliability of results to help ensure the integrity of the scientific literature, promote consistency between laboratories, and increase experimental transparency. {MIQE} is a set of guidelines that describe the minimum information necessary for evaluating {qPCR} experiments. Included is a checklist to accompany the initial submission of a manuscript to the publisher. By providing all relevant experimental conditions and assay characteristics, reviewers can assess the validity of the protocols used. Full disclosure of all reagents, sequences, and analysis methods is necessary to enable other investigators to reproduce results. {MIQE} details should be published either in abbreviated form or as an online supplement. {SUMMARY}: Following these guidelines will encourage better experimental practice, allowing more reliable and unequivocal interpretation of {qPCR} results.},
  Doi                      = {10.1373/clinchem.2008.112797},
  ISSN                     = {1530-8561},
  Keywords                 = {Humans; Molecular Diagnostic Techniques; Nucleic Acids; Polymerase chain reaction; Publishing; Reverse Transcription; Terminology as Topic; Time Factors},
  Language                 = {eng},
  Pmid                     = {19246619},
  Shorttitle               = {The {MIQE} guidelines}
}

@Article{chang_2012,
  Title                    = {{Diagnostic devices for isothermal nucleic acid amplification}},
  Author                   = {Chia-Chen Chang and Chien-Cheng Chen and Shih-Chung Wei and Hui-Hsin Lu and Yang-Hung Liang and Chii-Wann Lin},
  Journal                  = {Sensors (Basel, Switzerland)},
  Year                     = {2012},
  Note                     = {{PMID:} 22969402 {PMCID:} {PMC3436031}},
  Number                   = {6},
  Pages                    = {8319--8337},
  Volume                   = {12},

  Abstract                 = {Since the development of the polymerase chain reaction ({PCR)} technique, genomic information has been retrievable from lesser amounts of {DNA} than previously possible. {PCR-based} amplifications require high-precision instruments to perform temperature cycling reactions; further, they are cumbersome for routine clinical use. However, the use of isothermal approaches can eliminate many complications associated with thermocycling. The application of diagnostic devices for isothermal {DNA} amplification has recently been studied extensively. In this paper, we describe the basic concepts of several isothermal amplification approaches and review recent progress in diagnostic device development.},
  Doi                      = {10.3390/s120608319},
  ISSN                     = {1424-8220},
  Keywords                 = {Diagnostic Equipment; Humans; Nucleic Acid Amplification Techniques; Temperature},
  Language                 = {eng}
}

@Article{chou_rapid_2011,
  Title                    = {{Rapid {DNA} amplification in a capillary tube by natural convection with a single isothermal heater}},
  Author                   = {Wen Pin Chou and Ping Hei Chen and Ming Miao and Long Sheng Kuo and Shiou Hwei Yeh and Pei Jer Chen},
  Journal                  = {{BioTechniques}},
  Year                     = {2011},

  Month                    = jan,
  Number                   = {1},
  Pages                    = {52--57},
  Volume                   = {50},

  Abstract                 = {Herein we describe a simple platform for rapid {DNA} amplification using convection. Capillary convective {PCR} ({CCPCR}) heats the bottom of a capillary tube using a dry bath maintained at a fixed temperature of 95°C. The tube is then cooled by the surrounding air, creating a temperature gradient in which a sample can undergo {PCR} amplification by natural convection through reagent circulation. We demonstrate that altering the melting temperature of the primers relative to the lowest temperature in the tube affects amplification efficiency; adjusting the denaturation temperature of the amplicon relative to the highest temperature in the tube affects maximum amplicon size, with amplicon lengths of ≤500 bp possible. Based on these criteria, we successfully amplified {DNA} sequences from three different viral genomes in 30 min using {CCPCR}, with a sensitivity of {\textasciitilde}30 copies per reaction.},
  Doi                      = {10.2144/000113589},
  ISSN                     = {1940-9818},
  Keywords                 = {Convection; {DNA}; {DNA} Primers; Genome; Viral; Hot Temperature; Point-of-Care Systems; Polymerase chain reaction},
  Language                 = {eng},
  Pmid                     = {21231923}
}

@Article{cobb_1994,
  Title                    = {{A simple procedure for optimising the polymerase chain reaction ({PCR}) using modified Taguchi methods.}},
  Author                   = {B D Cobb and J M Clarkson},
  Journal                  = {Nucleic Acids Research},
  Year                     = {1994},

  Month                    = sep,
  Note                     = {00249 {PMID}: 7937094},
  Number                   = {18},
  Pages                    = {3801--3805},
  Volume                   = {22},

  Abstract                 = {Taguchi methods are used widely as the basis for development trials during industrial process design. Here, we describe their suitability for optimisation of the {PCR}. Unlike conventional strategies, these arrays revealed the effects and interactions of specific reaction components simultaneously using just a few reactions, negating the need for extensive experimental investigation. Reaction components which effected product yield were easily determined. In addition, this technique was applied to the qualitative investigation of {RAPD}-{PCR} profiles, where optimisation of the size and distribution of a number of products was determined.},
  ISSN                     = {0305-1048},
  Pmcid                    = {PMC308365},
  Url                      = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC308365},
  Urldate                  = {2014-11-27}
}

@Article{cobbs_2012,
  Title                    = {{Stepwise kinetic equilibrium models of quantitative polymerase chain reaction}},
  Author                   = {Gary Cobbs},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2012},

  Month                    = aug,
  Number                   = {1},
  Pages                    = {203},
  Volume                   = {13},

  Abstract                 = {Numerous models for use in interpreting quantitative {PCR} ({qPCR}) data are present in recent literature. The most commonly used models assume the amplification in {qPCR} is exponential and fit an exponential model with a constant rate of increase to a select part of the curve. Kinetic theory may be used to model the annealing phase and does not assume constant efficiency of amplification. Mechanistic models describing the annealing phase with kinetic theory offer the most potential for accurate interpretation of {qPCR} data. Even so, they have not been thoroughly investigated and are rarely used for interpretation of {qPCR} data. New results for kinetic modeling of {qPCR} are presented. {PMID}: 22897900},
  Copyright                = {2012 Cobbs; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-13-203},
  ISSN                     = {1471-2105},
  Keywords                 = {Kinetic model; {qPCR}; Quantitative polymerase chain reaction},
  Language                 = {en},
  Pmid                     = {22897900},
  Url                      = {http://www.biomedcentral.com/1471-2105/13/203/abstract},
  Urldate                  = {2014-07-01}
}

@InCollection{Dahlquist_2008,
  Title                    = {{Numerical Methods in Scientific Computing}},
  Author                   = {Germund Dahlquist and {\AA}ke Bj{\"o}rck},
  Publisher                = {{Society for Industrial and Applied Mathematics}},
  Year                     = {2008},
  Volume                   = {II},

  ISSN                     = {0898716446}
}

@Article{Dvinge_2009,
  Title                    = {{{HTqPCR}: High-throughput analysis and visualization of quantitative real-time {PCR} data in {R}}},
  Author                   = {Heidi Dvinge and Paul Bertone},
  Journal                  = {Bioinformatics},
  Year                     = {2009},
  Pages                    = {3325},
  Volume                   = {25(24)}
}

@Article{eilers_2003,
  Title                    = {{A Perfect Smoother}},
  Author                   = {Paul H. C. Eilers},
  Journal                  = {Analytical Chemistry},
  Year                     = {2003},

  Month                    = jul,
  Number                   = {14},
  Pages                    = {3631--3636},
  Volume                   = {75},

  Abstract                 = {The well-known and popular Savitzky?Golay filter has several disadvantages. A very attractive alternative is a smoother based on penalized least squares, extending ideas presented by Whittaker 80 years ago. This smoother is extremely fast, gives continuous control over smoothness, interpolates automatically, and allows fast leave-one-out cross-validation. It can be programmed in a few lines of Matlab code. Theory, implementation, and applications are presented.},
  Doi                      = {10.1021/ac034173t},
  ISSN                     = {0003-2700},
  Url                      = {http://dx.doi.org/10.1021/ac034173t},
  Urldate                  = {2014-08-22}
}

@Article{forcheh_2013,
  Title                    = {{The beadarrayFilter: An {R} Package to Filter Beads}},
  Author                   = {Anyiawung Chiara Forcheh and Geert Verbeke and Adetayo Kasim and Dan Lin and Ziv Shkedy and Willem Talloen and Hinrich W.H. Goehlmann and Lieven Clement},
  Journal                  = {The R Journal},
  Year                     = {2013},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {171--181},
  Volume                   = {5},

  Url                      = {http://journal.r-project.org/archive/2013-1/forcheh-verbeke-kasim-etal.pdf}
}

@Article{frank_2009,
  Title                    = {{{BARCRAWL} and {BARTAB:} software tools for the design and implementation of barcoded primers for highly multiplexed {DNA} sequencing}},
  Author                   = {Daniel N Frank},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2009},
  Note                     = {{PMID:} 19874596 {PMCID:} {PMC2777893}},
  Pages                    = {362},
  Volume                   = {10},

  Abstract                 = {{BACKGROUND:} Advances in automated {DNA} sequencing technology have greatly increased the scale of genomic and metagenomic studies. An increasingly popular means of increasing project throughput is by multiplexing samples during the sequencing phase. This can be achieved by covalently linking short, unique "barcode" {DNA} segments to genomic {DNA} samples, for instance through incorporation of barcode sequences in {PCR} primers. Although several strategies have been described to insure that barcode sequences are unique and robust to sequencing errors, these have not been integrated into the overall primer design process, thus potentially introducing bias into {PCR} amplification and/or sequencing steps. {RESULTS:} Barcrawl is a software program that facilitates the design of barcoded primers, for multiplexed high-throughput sequencing. The program bartab can be used to deconvolute {DNA} sequence datasets produced by the use of multiple barcoded primers. This paper describes the functions implemented by barcrawl and bartab and presents a proof-of-concept case study of both programs in which barcoded {rRNA} primers were designed and validated by high-throughput sequencing. {CONCLUSION:} Barcrawl and bartab can benefit researchers who are engaged in metagenomic projects that employ multiplexed specimen processing. The source code is released under the {GNU} general public license and can be accessed at http://www.phyloware.com.},
  Doi                      = {10.1186/1471-2105-10-362},
  ISSN                     = {1471-2105},
  Keywords                 = {Algorithms; Base Sequence; Computational Biology; {DNA} Primers; Genome; Molecular Sequence Data; Oligonucleotide Array Sequence Analysis; Sequence Analysis; {DNA}; Software},
  Language                 = {eng},
  Shorttitle               = {{BARCRAWL} and {BARTAB}}
}

@Article{gehlenborg_2013,
  Title                    = {{Nozzle: a report generation toolkit for data analysis pipelines}},
  Author                   = {Nils Gehlenborg and Michael S. Noble and Gad Getz and Lynda Chin and Peter J. Park},
  Journal                  = {Bioinformatics},
  Year                     = {2013},

  Month                    = apr,
  Number                   = {8},
  Pages                    = {1089--1091},
  Volume                   = {29},

  Abstract                 = {Summary: We have developed Nozzle, an R package that provides an Application Programming Interface to generate {HTML} reports with dynamic user interface elements. Nozzle was designed to facilitate summarization and rapid browsing of complex results in data analysis pipelines where multiple analyses are performed frequently on big datasets. The package can be applied to any project where user-friendly reports need to be created. Availability: The R package is available on {CRAN} at http://cran.r-project.org/package=Nozzle.R1. Examples and additional materials are available at http://gdac.broadinstitute.org/nozzle. The source code is also available at http://www.github.com/parklab/Nozzle. Contact: peter\_park@hms.harvard.edu Supplementary information: Supplementary data are available at Bioinformatics online.},
  Doi                      = {10.1093/bioinformatics/btt085},
  ISSN                     = {1367-4803, 1460-2059},
  Language                 = {en},
  Pmid                     = {23419376},
  Shorttitle               = {Nozzle},
  Url                      = {http://bioinformatics.oxfordjournals.org/content/29/8/1089},
  Urldate                  = {2014-09-20}
}

@Article{gentleman_2004,
  Title                    = {{Bioconductor: open software development for computational biology and bioinformatics}},
  Author                   = {Robert C Gentleman and Vincent J Carey and Douglas M Bates and Ben Bolstad and Marcel Dettling and Sandrine Dudoit and Byron Ellis and Laurent Gautier and Yongchao Ge and Jeff Gentry and Kurt Hornik and Torsten Hothorn and Wolfgang Huber and Stefano Iacus and Rafael Irizarry and Friedrich Leisch and Cheng Li and Martin Maechler and Anthony J Rossini and Gunther Sawitzki and Colin Smith and Gordon Smyth and Luke Tierney and Jean Y H Yang and Jianhua Zhang},
  Journal                  = {Genome biology},
  Year                     = {2004},
  Note                     = {{PMID:} 15461798 {PMCID:} {PMC545600}},
  Number                   = {10},
  Pages                    = {R80},
  Volume                   = {5},

  Abstract                 = {The Bioconductor project is an initiative for the collaborative creation of extensible software for computational biology and bioinformatics. The goals of the project include: fostering collaborative development and widespread use of innovative software, reducing barriers to entry into interdisciplinary scientific research, and promoting the achievement of remote reproducibility of research results. We describe details of our aims and methods, identify current challenges, compare Bioconductor to other open bioinformatics projects, and provide working examples.},
  Doi                      = {10.1186/gb-2004-5-10-r80},
  ISSN                     = {1465-6914},
  Keywords                 = {Computational Biology; Internet; Reproducibility of Results; Software},
  Language                 = {eng},
  Shorttitle               = {Bioconductor}
}

@Article{gerhard_2014,
  Title                    = {{Estimating marginal properties of quantitative real-time {PCR} data using nonlinear mixed models}},
  Author                   = {Daniel Gerhard and Melanie Bremer and Christian Ritz},
  Journal                  = {Biometrics},
  Year                     = {2014},

  Month                    = mar,
  Number                   = {1},
  Pages                    = {247--254},
  Volume                   = {70},

  Abstract                 = {A unified modeling framework based on a set of nonlinear mixed models is proposed for flexible modeling of gene expression in real-time {PCR} experiments. Focus is on estimating the marginal or population-based derived parameters: cycle thresholds and ΔΔc(t), but retaining the conditional mixed model structure to adequately reflect the experimental design. Additionally, the calculation of model-average estimates allows incorporation of the model selection uncertainty. The methodology is applied for estimating the differential expression of a phosphate transporter gene {OsPT}6 in rice in comparison to a reference gene at several states after phosphate resupply. In a small simulation study the performance of the proposed method is evaluated and compared to a standard method.},
  Doi                      = {10.1111/biom.12124},
  ISSN                     = {1541-0420},
  Language                 = {eng},
  Pmid                     = {24571556}
}

@Article{Gesmann_2011,
  Title                    = {{Using the Google Visualisation API with R}},
  Author                   = {Markus Gesmann and Diego de Castillo},
  Journal                  = {{The R Journal}},
  Year                     = {2011},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {40--44},
  Volume                   = {3},

  Url                      = {http://journal.r-project.org/archive/2011-2/RJournal_2011-2_Gesmann+de~Castillo.pdf}
}

@Article{guescini_2008,
  Title                    = {{A new real-time {PCR} method to overcome significant quantitative inaccuracy due to slight amplification inhibition}},
  Author                   = {Michele Guescini and Davide Sisti and Marco {BL} Rocchi and Laura Stocchi and Vilberto Stocchi},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2008},

  Month                    = jul,
  Note                     = {{PMID:} 18667053},
  Number                   = {1},
  Pages                    = {326},
  Volume                   = {9},

  Abstract                 = {Real-time {PCR} analysis is a sensitive {DNA} quantification technique that has recently gained considerable attention in biotechnology, microbiology and molecular diagnostics. Although, the cycle-threshold (Ct) method is the present "gold standard", it is far from being a standard assay. Uniform reaction efficiency among samples is the most important assumption of this method. Nevertheless, some authors have reported that it may not be correct and a slight {PCR} efficiency decrease of about 4\% could result in an error of up to 400\% using the Ct method. This reaction efficiency decrease may be caused by inhibiting agents used during nucleic acid extraction or copurified from the biological sample. {PMID:} 18667053},
  Copyright                = {2008 Guescini et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-9-326},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Url                      = {http://www.biomedcentral.com/1471-2105/9/326/abstract},
  Urldate                  = {2014-04-27}
}

@Misc{haned_forensim:_2013,
  Title                    = {{forensim: Statistical tools for the interpretation of forensic {DNA} mixtures}},

  Author                   = {Hinda Haned},
  Month                    = sep,
  Year                     = {2013},

  Abstract                 = {Statistical methods and simulation tools for the interpretation of forensic {DNA} mixtures},
  Copyright                = {{GPL-2} {\textbar} {GPL-3} [expanded from: {GPL} (≥ 2)]},
  Shorttitle               = {forensim},
  Url                      = {http://cran.r-project.org/web/packages/forensim/index.html},
  Urldate                  = {2014-05-02}
}

@Misc{pcrsim,
  Title                    = {{Simulation of the forensic DNA process}},

  Author                   = {Oskar Hansson},
  Month                    = dec,
  Year                     = {2013},

  Abstract                 = {pcrsim is a package for simulating the forensic DNA process. pcrsim() opens up a graphical user interface which allow the user to enter parameters required for the simulation. Once calibrated the program can be used to: reduce the laboratory work needed for validation of new STR kits, help develop methods for interpretation of DNA evidence, etc. This is a first version which is still experimental and under development.},
  Copyright                = {{GPL-2}},
  Shorttitle               = {{pcrsim}},
  Url                      = {http://cran.r-project.org/src/contrib/Archive/pcrsim},
  Urldate                  = {2014-05-03}
}

@InCollection{Harrell_2001,
  Title                    = {{Regression Modeling Strategies - with Applications to Linear Models, Logistic Regression, and Survival Analysis}},
  Author                   = {F E Harrell},
  Publisher                = {Springer, New York},
  Year                     = {2001},

  ISSN                     = {0-387-95232-2}
}

@Article{heckmann_2011,
  Title                    = {{{NORMA}-Gene: A simple and robust method for {qPCR} normalization based on target gene data}},
  Author                   = {Lars-Henrik Heckmann and Peter B. S{\o}rensen and Paul H. Krogh and Jesper G. S{\o}rensen},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2011},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {250},
  Volume                   = {12},

  Abstract                 = {Normalization of target gene expression, measured by real-time quantitative {PCR} ({qPCR}), is a requirement for reducing experimental bias and thereby improving data quality. The currently used normalization approach is based on using one or more reference genes. Yet, this approach extends the experimental work load and suffers from assumptions that may be difficult to meet and to validate. {PMID}: 21693017},
  Copyright                = {2011 Heckmann et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-12-250},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {21693017},
  Shorttitle               = {{NORMA}-Gene},
  Url                      = {http://www.biomedcentral.com/1471-2105/12/250/abstract},
  Urldate                  = {2014-07-01}
}

@Article{hofmann_2013,
  Title                    = {{Let Graphics Tell the Story - Datasets in R}},
  Author                   = {Heike Hofmann and Antony Unwin and Dianne Cook},
  Journal                  = {The R Journal},
  Year                     = {2013},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {117--130},
  Volume                   = {5},

  Abstract                 = {Graphics are good for showing the information in datasets and for complementing modelling. Sometimes graphics show information models miss, sometimes graphics help to make model results more understandable, and sometimes models show whether information from graphics has statistical support or not. It is the interplay of the two approaches that is valuable. Graphics could be used a lot more in R examples and we explore this idea with some datasets available in R packages.},
  Url                      = {http://journal.r-project.org/archive/2013-1/RJournal_2013-1_hofmann-unwin-cook.pdf}
}

@Article{huggett_2005,
  Title                    = {{Real-time {RT-PCR} normalisation; strategies and considerations}},
  Author                   = {J Huggett and K Dheda and S Bustin and A Zumla},
  Journal                  = {Genes and {I}mmunity},
  Year                     = {2005},

  Month                    = jun,
  Note                     = {{PMID:} 15815687},
  Number                   = {4},
  Pages                    = {279--284},
  Volume                   = {6},

  Abstract                 = {Real-time {RT-PCR} has become a common technique, no longer limited to specialist core facilities. It is in many cases the only method for measuring {mRNA} levels of vivo low copy number targets of interest for which alternative assays either do not exist or lack the required sensitivity. Benefits of this procedure over conventional methods for measuring {RNA} include its sensitivity, large dynamic range, the potential for high throughout as well as accurate quantification. To achieve this, however, appropriate normalisation strategies are required to control for experimental error introduced during the multistage process required to extract and process the {RNA.} There are many strategies that can be chosen; these include normalisation to sample size, total {RNA} and the popular practice of measuring an internal reference or housekeeping gene. However, these methods are frequently applied without appropriate validation. In this review we discuss the relative merits of different normalisation strategies and suggest a method of validation that will enable the measurement of biologically meaningful results.},
  Doi                      = {10.1038/sj.gene.6364190},
  ISSN                     = {1466-4879},
  Keywords                 = {Animals; {DNA}; Complementary; Gene Expression Profiling; Humans; Reference Standards; Reverse Transcriptase Polymerase Chain Reaction; {RNA}; Messenger},
  Language                 = {eng}
}

@Article{huggett_2013,
  Title                    = {{The digital {MIQE} guidelines: Minimum Information for Publication of Quantitative Digital {PCR} Experiments}},
  Author                   = {Jim F Huggett and Carole A Foy and Vladimir Benes and Kerry Emslie and Jeremy A Garson and Ross Haynes and Jan Hellemans and Mikael Kubista and Reinhold D Mueller and Tania Nolan and Michael W Pfaffl and Gregory L Shipley and Jo Vandesompele and Carl T Wittwer and Stephen A Bustin},
  Journal                  = {Clinical {C}hemistry},
  Year                     = {2013},

  Month                    = jun,
  Note                     = {{PMID:} 23570709},
  Number                   = {6},
  Pages                    = {892--902},
  Volume                   = {59},

  Abstract                 = {There is growing interest in digital {PCR} ({dPCR)} because technological progress makes it a practical and increasingly affordable technology. {dPCR} allows the precise quantification of nucleic acids, facilitating the measurement of small percentage differences and quantification of rare variants. {dPCR} may also be more reproducible and less susceptible to inhibition than quantitative real-time {PCR} ({qPCR).} Consequently, {dPCR} has the potential to have a substantial impact on research as well as diagnostic applications. However, as with {qPCR}, the ability to perform robust meaningful experiments requires careful design and adequate controls. To assist independent evaluation of experimental data, comprehensive disclosure of all relevant experimental details is required. To facilitate this process we present the Minimum Information for Publication of Quantitative Digital {PCR} Experiments guidelines. This report addresses known requirements for {dPCR} that have already been identified during this early stage of its development and commercial implementation. Adoption of these guidelines by the scientific community will help to standardize experimental protocols, maximize efficient utilization of resources, and enhance the impact of this promising new technology.},
  Doi                      = {10.1373/clinchem.2013.206375},
  ISSN                     = {1530-8561},
  Keywords                 = {Computers; Guidelines as Topic; Real-Time Polymerase Chain Reaction},
  Language                 = {eng},
  Shorttitle               = {The digital {MIQE} guidelines}
}

@Article{huntley_2013,
  Title                    = {{{ReportingTools}: an automated result processing and presentation toolkit for high-throughput genomic analyses}},
  Author                   = {Melanie A. Huntley and Jessica L. Larson and Christina Chaivorapol and Gabriel Becker and Michael Lawrence and Jason A. Hackney and Joshua S. Kaminker},
  Journal                  = {Bioinformatics},
  Year                     = {2013},

  Month                    = dec,
  Number                   = {24},
  Pages                    = {3220--3221},
  Volume                   = {29},

  Abstract                 = {Summary: It is common for computational analyses to generate large amounts of complex data that are difficult to process and share with collaborators. Standard methods are needed to transform such data into a more useful and intuitive format. We present {ReportingTools}, a Bioconductor package, that automatically recognizes and transforms the output of many common Bioconductor packages into rich, interactive, {HTML}-based reports. Reports are not generic, but have been individually designed to reflect content specific to the result type detected. Tabular output included in reports is sortable, filterable and searchable and contains context-relevant hyperlinks to external databases. Additionally, in-line graphics have been developed for specific analysis types and are embedded by default within table rows, providing a useful visual summary of underlying raw data. {ReportingTools} is highly flexible and reports can be easily customized for specific applications using the well-defined {API}. Availability: The {ReportingTools} package is implemented in R and available from Bioconductor (version ≥ 2.11) at the {URL}: http://bioconductor.org/packages/release/bioc/html/{ReportingTools}.html. Installation instructions and usage documentation can also be found at the above {URL}. Contact: hackney.jason@gene.com or kaminker.josh@gene.com},
  Doi                      = {10.1093/bioinformatics/btt551},
  ISSN                     = {1367-4803, 1460-2059},
  Language                 = {en},
  Pmid                     = {24078713},
  Shorttitle               = {{ReportingTools}},
  Url                      = {http://bioinformatics.oxfordjournals.org/content/29/24/3220},
  Urldate                  = {2014-09-20}
}

@Article{Jacobs_2014,
  Title                    = {PyRDM: A Python-based library for automating the management and online publication of scientific software and data},
  Author                   = {Christian Jacobs and Alexandros Avdis and Gerard Gorman and Matthew Piggott},
  Journal                  = {Journal of Open Research Software},
  Year                     = {2014},
  Number                   = {1},
  Volume                   = {2},

  Abstract                 = {The recomputability and reproducibility of results from scientific software requires access to both the source code and all associated input and output data. However, the full collection of these resources often does not accompany the key findings published in journal articles, thereby making it difficult or impossible for the wider scientific community to verify the correctness of a result or to build further research on it. This paper presents a new Python-based library, PyRDM, whose functionality aims to automate the process of sharing the software and data via online, citable repositories such as Figshare. The library is integrated into the workflow of an open-source computational fluid dynamics package, Fluidity, to demonstrate an example of its usage.},
  ISSN                     = {2049-9647},
  Keywords                 = {scientific software; data; automated publication; reproducibility; recomputability; Python; Figshare; GitHub},
  Url                      = {http://openresearchsoftware.metajnl.com/article/view/jors.bj}
}

@Article{Karatzoglou_2004,
  Title                    = {{kernlab - An {S4} Package for Kernel Methods in {R}}},
  Author                   = {Alexandros Karatzoglou and Alexandros Smola and Kurt Hornik and Achim Zeileis},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2004},

  Month                    = nov,
  Number                   = {9},
  Pages                    = {1--20},
  Volume                   = {11},

  Accepted                 = {2004-11-02},
  Bibdate                  = {2004-11-02},
  Coden                    = {JSSOBK},
  Day                      = {2},
  ISSN                     = {1548-7660},
  Submitted                = {2004-08-15},
  Url                      = {http://www.jstatsoft.org/v11/i09}
}

@Article{Kloke_2012,
  Title                    = {{Rfit: Rank-based Estimation for Linear Models}},
  Author                   = {John D. Kloke and Joseph W. McKean},
  Journal                  = {{The R Journal}},
  Year                     = {2012},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {57--64},
  Volume                   = {4},

  Url                      = {http://journal.r-project.org/archive/2012-2/RJournal_2012-2_Kloke+McKean.pdf}
}

@Article{Knuth1984,
  Title                    = {{Literate Programming}},
  Author                   = {D. E. Knuth},
  Journal                  = {The Computer Journal},
  Year                     = {1984},
  Number                   = {2},
  Pages                    = {97--111},
  Volume                   = {27},

  Abstract                 = {The author and his associates have been experimenting for the past several years with a programming language and documentation system called WEB. This paper presents WEB by example, and discusses why the new system appears to be an improvement over previous ones.},
  Doi                      = {10.1093/comjnl/27.2.97},
  Eprint                   = {http://comjnl.oxfordjournals.org/content/27/2/97.full.pdf+html},
  Url                      = {http://comjnl.oxfordjournals.org/content/27/2/97.abstract}
}

@Article{Koenker_2008,
  Title                    = {{Censored Quantile Regression Redux}},
  Author                   = {Roger Koenker},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2008},

  Month                    = jul,
  Number                   = {6},
  Pages                    = {1--25},
  Volume                   = {27},

  Accepted                 = {2008-05-26},
  Bibdate                  = {2008-05-26},
  Coden                    = {JSSOBK},
  Day                      = {29},
  ISSN                     = {1548-7660},
  Submitted                = {2008-02-20},
  Url                      = {http://www.jstatsoft.org/v27/i06}
}

@Manual{SLqPCR_2007,
  Title                    = {{SLqPCR: Functions for analysis of real-time quantitative {PCR} data at SIRS-Lab GmbH}},

  Address                  = {Jena},
  Author                   = {M. Kohl},
  Organization             = {SIRS-Lab GmbH},
  Year                     = {2007},

  Language                 = {English},
  Type                     = {R package},
  Url                      = {www.sirs-lab.com}
}

@Manual{Komsta_2011,
  Title                    = {{outliers: Tests for outliers}},
  Author                   = {Lukasz Komsta},
  Note                     = {R package version 0.14},
  Year                     = {2011},

  Url                      = {http://CRAN.R-project.org/package=outliers}
}

@Article{larionov_2005,
  Title                    = {{A standard curve based method for relative real time {PCR} data processing}},
  Author                   = {Alexey Larionov and Andreas Krause and William Miller},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2005},

  Month                    = mar,
  Note                     = {{PMID:} 15780134},
  Number                   = {1},
  Pages                    = {62},
  Volume                   = {6},

  Abstract                 = {Currently real time {PCR} is the most precise method by which to measure gene expression. The method generates a large amount of raw numerical data and processing may notably influence final results. The data processing is based either on standard curves or on {PCR} efficiency assessment. At the moment, the {PCR} efficiency approach is preferred in relative {PCR} whilst the standard curve is often used for absolute {PCR.} However, there are no barriers to employ standard curves for relative {PCR.} This article provides an implementation of the standard curve method and discusses its advantages and limitations in relative real time {PCR.} {PMID:} 15780134},
  Copyright                = {2005 Larionov et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-6-62},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Url                      = {http://www.biomedcentral.com/1471-2105/6/62/abstract},
  Urldate                  = {2014-05-02}
}

@Article{Leeper_2014,
  Title                    = {{Archiving Reproducible Research and Dataverse with {R}}},
  Author                   = {Thomas J. Leeper},
  Journal                  = {The R Journal},
  Year                     = {2014},
  Number                   = {1},
  Pages                    = {NN--NN},
  Volume                   = {6},

  Url                      = {http://journal.r-project.org/archive/accepted/leeper.pdf}
}

@Article{lefever_2009,
  Title                    = {{{RDML:} structured language and reporting guidelines for real-time quantitative {PCR} data}},
  Author                   = {Steve Lefever and Jan Hellemans and Filip Pattyn and Daniel R Przybylski and Chris Taylor and Ren{\'e} Geurts and Andreas Untergasser and Jo Vandesompele and {{RDML} consortium}},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2009},

  Month                    = apr,
  Note                     = {{PMID:} 19223324 {PMCID:} {PMC2673419}},
  Number                   = {7},
  Pages                    = {2065--2069},
  Volume                   = {37},

  Abstract                 = {The {XML-based} Real-Time {PCR} Data Markup Language ({RDML)} has been developed by the {RDML} consortium (http://www.rdml.org) to enable straightforward exchange of {qPCR} data and related information between {qPCR} instruments and third party data analysis software, between colleagues and collaborators and between experimenters and journals or public repositories. We here also propose data related guidelines as a subset of the Minimum Information for Publication of Quantitative Real-Time {PCR} Experiments ({MIQE)} to guarantee inclusion of key data information when reporting experimental results.},
  Doi                      = {10.1093/nar/gkp056},
  ISSN                     = {1362-4962},
  Keywords                 = {Guidelines as Topic; Internet; Polymerase chain reaction; Software; Terminology as Topic},
  Language                 = {eng},
  Shorttitle               = {{RDML}}
}

@Article{liu_2002,
  Title                    = {{A new quantitative method of real time reverse transcription polymerase chain reaction assay based on simulation of polymerase chain reaction kinetics}},
  Author                   = {Weihong Liu and David A Saint},
  Journal                  = {Analytical {B}iochemistry},
  Year                     = {2002},

  Month                    = mar,
  Note                     = {{PMID:} 11846375},
  Number                   = {1},
  Pages                    = {52--59},
  Volume                   = {302},

  Abstract                 = {Real-time reverse transcription ({RT)} {PCR} is currently the most sensitive method for the detection of low-abundance {mRNAs.} Two relative quantitative methods have been adopted: the standard curve method and the comparative C(T) method. The latter is used when the amplification efficiency of a reference gene is equal to that of the target gene; otherwise the standard curve method is applied. Based on the simulation of kinetic process of real-time {PCR}, we have developed a new method for quantitation and normalization of gene transcripts. In our method, the amplification efficiency for each individual reaction is calculated from the kinetic curve, and the initial amount of gene transcript is derived and normalized. Simulation demonstrated that our method is more accurate than the comparative C(T) method and would save more time than the relative standard curve method. We have used the new method to quantify gene expression levels of nine two-pore potassium channels. The relative levels of gene expression revealed by our quantitative method were broadly consistent with those estimated by routine {RT-PCR}, but the results also showed that amplification efficiencies varied from gene to gene and from sample to sample. Our method provides a simple and accurate approach to quantifying gene expression level with the advantages that neither construction of standard curve nor validation experiments are needed.},
  Doi                      = {10.1006/abio.2001.5530},
  ISSN                     = {0003-2697},
  Keywords                 = {Animals; Kinetics; Mice; Polymerase chain reaction; Rats; Reverse Transcriptase Polymerase Chain Reaction},
  Language                 = {eng}
}

@Article{liu_2014,
  Title                    = {{An R package that automatically collects and archives details for reproducible computing}},
  Author                   = {Zhifa Liu and Stan Pounds},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2014},

  Month                    = may,
  Number                   = {1},
  Pages                    = {138},
  Volume                   = {15},

  Abstract                 = {It is scientifically and ethically imperative that the results of statistical analysis of biomedical research data be computationally reproducible in the sense that the reported results can be easily recapitulated from the study data. Some statistical analyses are computationally a function of many data files, program files, and other details that are updated or corrected over time. In many applications, it is infeasible to manually maintain an accurate and complete record of all these details about a particular analysis. {PMID}: 24886202},
  Copyright                = {2014 Liu and Pounds; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-15-138},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {24886202},
  Url                      = {http://www.biomedcentral.com/1471-2105/15/138/abstract},
  Urldate                  = {2014-07-01}
}

@Article{livak_2001,
  Title                    = {{Analysis of relative gene expression data using real-time quantitative {PCR} and the 2(-Delta Delta C(T)) Method}},
  Author                   = {K J Livak and T D Schmittgen},
  Journal                  = {Methods (San Diego, Calif.)},
  Year                     = {2001},

  Month                    = dec,
  Note                     = {{PMID:} 11846609},
  Number                   = {4},
  Pages                    = {402--408},
  Volume                   = {25},

  Abstract                 = {The two most commonly used methods to analyze data from real-time, quantitative {PCR} experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the {PCR} signal to a standard curve. Relative quantification relates the {PCR} signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative {PCR} experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be @article{wilhelm\_real-time\_2003, title = {Real-time {PCR-based} method for the estimation of genome sizes}, volume = {31}, issn = {0305-1048}, url={http://www.ncbi.nlm.nih.gov/pmc/articles/PMC156059}, abstract = {The fast and reliable estimation of the genome sizes of various species would allow for a systematic analysis of many organisms and could reveal insights into evolutionary processes. Many methods for the estimation of genome sizes have already been described. The classical methods are based on the determination of the phosphate content in the {DNA} backbone of total {DNA} isolated from a defined number of cells or on reassociation kinetics of high molecular weight genomic {DNA} (c0t assay). More recent techniques employ {DNA-specific} fluorescent dyes in flow cytometry analysis, image analysis or absorption cytometry after Feulgen staining. The method presented here is based on the absolute quantification of genetic elements in a known amount (mass) of genomic {DNA} by real-time quantitative {PCR.} The method was evaluated on three different eukaryotic species, Saccharomyces cerevisiae (12.1 Mb), Xiphophorus maculatus (550 Mb) and Homo sapiens sapiens (2.9 Gb), and found to be fast, highly accurate and reliable.}, number = {10}, urldate = {2014-04-27}, journal = {Nucleic {A}cids {R}esearch}, author = {Wilhelm, Jochen and Pingoud, Alfred and Hahn, Meinhard}, month = May, year = {2003}, note = {{PMID:} 12736322 {PMCID:} {PMC156059}}, pages = {e56} } useful in the analysis of real-time, quantitative {PCR} data.},
  Doi                      = {10.1006/meth.2001.1262},
  ISSN                     = {1046-2023},
  Keywords                 = {Algorithms; Brain; Cell Line; {DNA}; Complementary; Humans; Polymerase chain reaction; Reverse Transcriptase Polymerase Chain Reaction; Time Factors},
  Language                 = {eng}
}

@Article{mallona_2011,
  Title                    = {{{pcrEfficiency}: a Web tool for {PCR} amplification efficiency prediction}},
  Author                   = {Izaskun Mallona and Julia Weiss and Marcos Egea-Cortines},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2011},

  Month                    = oct,
  Number                   = {1},
  Pages                    = {404},
  Volume                   = {12},

  Abstract                 = {Relative calculation of differential gene expression in quantitative {PCR} reactions requires comparison between amplification experiments that include reference genes and genes under study. Ignoring the differences between their efficiencies may lead to miscalculation of gene expression even with the same starting amount of template. Although there are several tools performing {PCR} primer design, there is no tool available that predicts {PCR} efficiency for a given amplicon and primer pair. {PMID}: 22014212},
  Copyright                = {2011 Mallona et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-12-404},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {22014212},
  Shorttitle               = {{pcrEfficiency}},
  Url                      = {http://www.biomedcentral.com/1471-2105/12/404/abstract},
  Urldate                  = {2014-07-01}
}

@Article{mao_2007,
  Title                    = {{Characterization of {EvaGreen} and the implication of its physicochemical properties for {qPCR} applications}},
  Author                   = {Fei Mao and Wai-Yee Leung and Xing Xin},
  Journal                  = {{BMC} Biotechnology},
  Year                     = {2007},

  Month                    = nov,
  Pages                    = {76},
  Volume                   = {7},

  Abstract                 = {Background {EvaGreen} ({EG}) is a newly developed {DNA}-binding dye that has recently been used in quantitative real-time {PCR} ({qPCR}), post-{PCR} {DNA} melt curve analysis and several other applications. However, very little is known about the physicochemical properties of the dye and their relevance to the applications, particularly to {qPCR} and post {PCR} {DNA} melt curve analysis. In this paper, we characterized {EG} along with a widely used {qPCR} dye, {SYBR} Green I ({SG}), for their {DNA}-binding properties and stability, and compared their performance in {qPCR} under a variety of conditions. Results This study systematically compared the {DNA} binding profiles of the two dyes under different conditions and had these findings: a) {EG} had a lower binding affinity for both double-stranded {DNA} ({dsDNA}) and single-stranded {DNA} ({ssDNA}) than {SG}; b) {EG} showed no apparent preference for either {GC}- or {AT}-rich sequence while {SG} had a slight preference for {AT}-rich sequence; c) both dyes showed substantially lower affinity toward {ssDNA} than toward {dsDNA} and even lower affinity toward shorter {ssDNA} fragments except that this trend was more pronounced for {EG}. Our results also demonstrated that {EG} was stable both under {PCR} condition and during routine storage and handling. In the comparative {qPCR} study, both {EG} and {SG} exhibited {PCR} interference when used at high dye concentration, as evident from delayed Ct and/or nonspecific product formation. The problem worsened when the chain extension time was shortened or when the amplicon size was relatively long ({\textgreater}500 bp). However, {qPCR} using {EG} tolerated a significantly higher dye concentration, thus permitting a more robust {PCR} signal as well as a sharper and stronger {DNA} melt peak. These differences in {qPCR} performance between the two dyes are believed to be attributable to their differences in {DNA} binding profiles. Conclusion These findings suggest that an ideal {qPCR} dye should possess several {DNA}-binding characteristics, including a "just right" affinity for {dsDNA} and low or no affinity for {ssDNA} and short {DNA} fragments. The favorable {DNA}-binding profile of {EG}, coupled with its good stability and instrument-compatibility, should make {EG} a promising dye for {qPCR} and related applications.},
  Doi                      = {10.1186/1472-6750-7-76},
  ISSN                     = {1472-6750},
  Pmcid                    = {PMC2213645},
  Pmid                     = {17996102},
  Url                      = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2213645},
  Urldate                  = {2014-03-23}
}

@Article{mccall_2014,
  Title                    = {{On non-detects in {qPCR} data}},
  Author                   = {Matthew N. McCall and Helene R. McMurray and Hartmut Land and Anthony Almudevar},
  Journal                  = {Bioinformatics (Oxford, England)},
  Year                     = {2014},

  Month                    = apr,

  Abstract                 = {{MOTIVATION}: Quantitative real-time {PCR} ({qPCR}) is one of the most widely used methods to measure gene expression. Despite extensive research in {qPCR} laboratory protocols, normalization and statistical analysis, little attention has been given to {qPCR} non-detects-those reactions failing to produce a minimum amount of signal. {RESULTS}: We show that the common methods of handling {qPCR} non-detects lead to biased inference. Furthermore, we show that non-detects do not represent data missing completely at random and likely represent missing data occurring not at random. We propose a model of the missing data mechanism and develop a method to directly model non-detects as missing data. Finally, we show that our approach results in a sizeable reduction in bias when estimating both absolute and differential gene expression. Availability and implementation: The proposed algorithm is implemented in the R package, nondetects. This package also contains the raw data for the three example datasets used in this manuscript. The package is freely available at http://mnmccall.com/software and as part of the Bioconductor project. {CONTACT}: mccallm@gmail.com.},
  Doi                      = {10.1093/bioinformatics/btu239},
  ISSN                     = {1367-4811},
  Language                 = {{ENG}},
  Pmid                     = {24764462}
}

@Article{mehra_2005,
  Title                    = {{A kinetic model of quantitative real-time polymerase chain reaction}},
  Author                   = {Sarika Mehra and Wei-Shou Hu},
  Journal                  = {Biotechnology and bioengineering},
  Year                     = {2005},

  Month                    = sep,
  Note                     = {{PMID:} 15986490},
  Number                   = {7},
  Pages                    = {848--860},
  Volume                   = {91},

  Abstract                 = {Real-time polymerase chain reaction ({PCR)} is one of the most sensitive and accurate methods for quantifying transcript levels especially for those expressed at low abundance. The selective amplification of target {DNA} over multiple cycles allows its initial concentration to be determined. The amplification rate is a complex interplay of the operating conditions, initial reactant concentrations, and reaction rate constants. Experimentally, the compounded effect of all factors is quantified in terms of an effective efficiency, which is estimated by curve fitting to the amplification data. We present a comprehensive model of {PCR} to study the effect of various reactant concentrations on the amplification efficiency. The model is used to calculate the kinetic progression of the target {DNA} concentration with cycle number under conditions when different species are stoichiometrically or kinetically limiting. The reaction efficiency remains constant for the initial cycles. As the primer concentration becomes limiting, the efficiency is marked by a gradual decrease. This is in contrast to a steep decline under nucleotide limiting conditions. Under some conditions, commonly used experimentally, increasing primer concentration has the adverse effect of reducing the final amplified template concentration. This phenomenon seen at times experimentally is explained by the simulation results under rate limiting enzyme concentrations. Primer dimer formation is shown to significantly affect the reaction rates, effective efficiency, and the estimated initial concentrations. This model, by describing the interplay of the many operating variables, will be a useful tool in designing {PCR} conditions and evaluating its results.},
  Doi                      = {10.1002/bit.20555},
  ISSN                     = {0006-3592},
  Keywords                 = {Computer Simulation; {DNA}; Kinetics; Models; Chemical; Polymerase chain reaction; Templates; Genetic},
  Language                 = {eng}
}

@Article{mehra_kinetic_2005,
  Title                    = {{A kinetic model of quantitative real-time polymerase chain reaction}},
  Author                   = {Sarika Mehra and Wei-Shou Hu},
  Journal                  = {Biotechnology and bioengineering},
  Year                     = {2005},

  Month                    = sep,
  Note                     = {{PMID:} 15986490},
  Number                   = {7},
  Pages                    = {848--860},
  Volume                   = {91},

  Abstract                 = {Real-time polymerase chain reaction ({PCR)} is one of the most sensitive and accurate methods for quantifying transcript levels especially for those expressed at low abundance. The selective amplification of target {DNA} over multiple cycles allows its initial concentration to be determined. The amplification rate is a complex interplay of the operating conditions, initial reactant concentrations, and reaction rate constants. Experimentally, the compounded effect of all factors is quantified in terms of an effective efficiency, which is estimated by curve fitting to the amplification data. We present a comprehensive model of {PCR} to study the effect of various reactant concentrations on the amplification efficiency. The model is used to calculate the kinetic progression of the target {DNA} concentration with cycle number under conditions when different species are stoichiometrically or kinetically limiting. The reaction efficiency remains constant for the initial cycles. As the primer concentration becomes limiting, the efficiency is marked by a gradual decrease. This is in contrast to a steep decline under nucleotide limiting conditions. Under some conditions, commonly used experimentally, increasing primer concentration has the adverse effect of reducing the final amplified template concentration. This phenomenon seen at times experimentally is explained by the simulation results under rate limiting enzyme concentrations. Primer dimer formation is shown to significantly affect the reaction rates, effective efficiency, and the estimated initial concentrations. This model, by describing the interplay of the many operating variables, will be a useful tool in designing {PCR} conditions and evaluating its results.},
  Doi                      = {10.1002/bit.20555},
  ISSN                     = {0006-3592},
  Keywords                 = {Computer Simulation; {DNA}; Kinetics; Models; Chemical; Polymerase chain reaction; Templates; Genetic},
  Language                 = {eng}
}

@Article{michna_2013,
  Title                    = {{RNetCDF -- A Package for Reading and Writing NetCDF Datasets}},
  Author                   = {Pavel Michna and Milton Woods},
  Journal                  = {The R Journal},
  Year                     = {2013},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {29--37},
  Volume                   = {5},

  Url                      = {http://journal.r-project.org/archive/2013-2/michna-woods.pdf}
}

@Article{Murrell_2012,
  Title                    = {{It's Not What You Draw, It's What You Don't Draw}},
  Author                   = {Paul Murrell},
  Journal                  = {{The R Journal}},
  Year                     = {2012},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {13--18},
  Volume                   = {4},

  Url                      = {http://journal.r-project.org/archive/2012-2/RJournal_2012-2_Murrell2.pdf}
}

@Article{neve_2014,
  Title                    = {{{unifiedWMWqPCR}: the unified Wilcoxon--Mann--Whitney test for analyzing {RT}-{qPCR} data in R}},
  Author                   = {Jan De Neve and Joris Meys and Jean-Pierre Ottoy and Lieven Clement and Olivier Thas},
  Journal                  = {Bioinformatics},
  Year                     = {2014},

  Month                    = sep,
  Number                   = {17},
  Pages                    = {2494--2495},
  Volume                   = {30},

  Abstract                 = {Motivation: Recently, De Neve et al. proposed a modification of the Wilcoxon--Mann--Whitney ({WMW}) test for assessing differential expression based on {RT}-{qPCR} data. Their test, referred to as the unified {WMW} ({uWMW}) test, incorporates a robust and intuitive normalization and quantifies the probability that the expression from one treatment group exceeds the expression from another treatment group. However, no software package for this test was available yet. Results: We have developed a Bioconductor package for analyzing {RT}-{qPCR} data with the {uWMW} test. The package also provides graphical tools for visualizing the effect sizes. Availability and implementation: The {unifiedWMWqPCR} package and its user documentation can be obtained through Bioconductor. Contact: {JanR}.{DeNeve}@{UGent}.be},
  Doi                      = {10.1093/bioinformatics/btu313},
  ISSN                     = {1367-4803, 1460-2059},
  Language                 = {en},
  Pmid                     = {24794933},
  Shorttitle               = {{unifiedWMWqPCR}},
  Url                      = {http://bioinformatics.oxfordjournals.org/content/30/17/2494},
  Urldate                  = {2014-09-21}
}

@Article{Nie_2012,
  Title                    = {{The crs Package: Nonparametric Regression Splines for Continuous and Categorical Predictors}},
  Author                   = {Zhenghua Nie and Jeffrey S Racine},
  Journal                  = {{The R Journal}},
  Year                     = {2012},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {48--56},
  Volume                   = {4},

  Url                      = {http://journal.r-project.org/archive/2012-2/RJournal_2012-2_Nie+S~Racine.pdf}
}

@Article{pabinger_2014,
  Title                    = {{A survey of tools for the analysis of quantitative {PCR} ({qPCR}) data}},
  Author                   = {Stephan Pabinger and Stefan R{\"o}diger and Albert Kriegner and Klemens Vierlinger and Andreas Weinh{\"a}usel},
  Journal                  = {Biomolecular Detection and Quantification},
  Year                     = {2014},

  Month                    = sep,
  Note                     = {00000},
  Number                   = {1},
  Pages                    = {23--33},
  Volume                   = {1},

  Abstract                 = {Real-time quantitative polymerase-chain-reaction ({qPCR}) is a standard technique in most laboratories used for various applications in basic research. Analysis of {qPCR} data is a crucial part of the entire experiment, which has led to the development of a plethora of methods. The released tools either cover specific parts of the workflow or provide complete analysis solutions. Here, we surveyed 27 open-access software packages and tools for the analysis of {qPCR} data. The survey includes 8 Microsoft Windows, 5 web-based, 9 R-based and 5 tools from other platforms. Reviewed packages and tools support the analysis of different {qPCR} applications, such as {RNA} quantification, {DNA} methylation, genotyping, identification of copy number variations, and digital {PCR}. We report an overview of the functionality, features and specific requirements of the individual software tools, such as data exchange formats, availability of a graphical user interface, included procedures for graphical data presentation, and offered statistical methods. In addition, we provide an overview about quantification strategies, and report various applications of {qPCR}. Our comprehensive survey showed that most tools use their own file format and only a fraction of the currently existing tools support the standardized data exchange format {RDML}. To allow a more streamlined and comparable analysis of {qPCR} data, more vendors and tools need to adapt the standardized format to encourage the exchange of data between instrument software, analysis tools, and researchers.},
  Doi                      = {10.1016/j.bdq.2014.08.002},
  ISSN                     = {2214-7535},
  Keywords                 = {Data analysis; {MIQE}; {qPCR}; {RDML}; Software; Tools},
  Url                      = {http://www.sciencedirect.com/science/article/pii/S2214753514000059},
  Urldate                  = {2014-11-27}
}

@Article{pabinger_2009,
  Title                    = {{{QPCR}: Application for real-time {PCR} data management and analysis}},
  Author                   = {Stephan Pabinger and Gerhard G. Thallinger and Ren{\'e} Snajder and Heiko Eichhorn and Robert Rader and Zlatko Trajanoski},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2009},

  Month                    = aug,
  Number                   = {1},
  Pages                    = {268},
  Volume                   = {10},

  Abstract                 = {Since its introduction quantitative real-time polymerase chain reaction ({qPCR}) has become the standard method for quantification of gene expression. Its high sensitivity, large dynamic range, and accuracy led to the development of numerous applications with an increasing number of samples to be analyzed. Data analysis consists of a number of steps, which have to be carried out in several different applications. Currently, no single tool is available which incorporates storage, management, and multiple methods covering the complete analysis pipeline. {PMID}: 19712446},
  Copyright                = {2009 Pabinger et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-10-268},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {19712446},
  Shorttitle               = {{QPCR}},
  Url                      = {http://www.biomedcentral.com/1471-2105/10/268/abstract},
  Urldate                  = {2014-07-01}
}

@Manual{pan_2012,
  Title                    = {{{qPCR}.{CT}: {qPCR} data analysis and plot package}},
  Author                   = {Yuzhuo Pan and Xiaoyu Yan and Junxin Li},
  Month                    = oct,
  Year                     = {2012},

  Abstract                 = {use 2{\textasciicircum}{ddCT} methods calculate the relative gene expression, data file can be export from bio-rad qpcr machine, the results can be plot with errorbar. ver 1.1 add {GroupPlot} function, can plot all the groups once.},
  Copyright                = {{GPL}-2},
  Shorttitle               = {{qPCR}.{CT}},
  Url                      = {http://cran.r-project.org/web/packages/qPCR.CT/index.html},
  Urldate                  = {2014-04-27}
}

@Article{peirson_2003,
  Title                    = {{Experimental validation of novel and conventional approaches to quantitative real-time {PCR} data analysis}},
  Author                   = {Stuart N Peirson and Jason N Butler and Russell G Foster},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2003},

  Month                    = jul,
  Note                     = {{PMID:} 12853650 {PMCID:} {PMC167648}},
  Number                   = {14},
  Pages                    = {e73},
  Volume                   = {31},

  Abstract                 = {Real-time {PCR} is being used increasingly as the method of choice for {mRNA} quantification, allowing rapid analysis of gene expression from low quantities of starting template. Despite a wide range of approaches, the same principles underlie all data analysis, with standard approaches broadly classified as either absolute or relative. In this study we use a variety of absolute and relative approaches of data analysis to investigate nocturnal c-fos expression in wild-type and retinally degenerate mice. In addition, we apply a simple algorithm to calculate the amplification efficiency of every sample from its amplification profile. We confirm that nocturnal c-fos expression in the rodent eye originates from the photoreceptor layer, with around a 5-fold reduction in nocturnal c-fos expression in mice lacking rods and cones. Furthermore, we illustrate that differences in the results obtained from absolute and relative approaches are underpinned by differences in the calculated {PCR} efficiency. By calculating the amplification efficiency from the samples under analysis, comparable results may be obtained without the need for standard curves. We have automated this method to provide a means of streamlining the real-time {PCR} process, enabling analysis of experimental samples based upon their own reaction kinetics rather than those of artificial standards.},
  ISSN                     = {1362-4962},
  Keywords                 = {Actins; Animals; {DNA}; Complementary; Gene Expression; Mice; Mice; Mutant Strains; Polymerase chain reaction; Proto-Oncogene Proteins c-fos; Reference Standards; Reproducibility of Results; {RNA}; Messenger},
  Language                 = {eng}
}

@Article{perkins_2012,
  Title                    = {{{ReadqPCR} and {NormqPCR:} {R} packages for the reading, quality checking and normalisation of {RT-qPCR} quantification cycle (Cq) data}},
  Author                   = {James R. Perkins and John M. Dawes and Steve B. {McMahon} and David {LH} Bennett and Christine Orengo and Matthias Kohl},
  Journal                  = {{BMC} Genomics},
  Year                     = {2012},

  Month                    = jul,
  Note                     = {{PMID:} 22748112},
  Number                   = {1},
  Pages                    = {296},
  Volume                   = {13},

  Abstract                 = {Measuring gene transcription using real-time reverse transcription polymerase chain reaction ({RT-qPCR)} technology is a mainstay of molecular biology. Technologies now exist to measure the abundance of many transcripts in parallel. The selection of the optimal reference gene for the normalisation of this data is a recurring problem, and several algorithms have been developed in order to solve it. So far nothing in R exists to unite these methods, together with other functions to read in and normalise the data using the chosen reference gene(s). {PMID:} 22748112},
  Copyright                = {2012 Perkins et al.; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2164-13-296},
  Language                 = {en},
  Shorttitle               = {{ReadqPCR} and {NormqPCR}},
  Url                      = {http://www.biomedcentral.com/1471-2164/13/296/abstract},
  Urldate                  = {2014-04-27}
}

@Article{Pramana_2010,
  Title                    = {{IsoGene: An R Package for Analyzing Dose-response Studies in Microarray Experiments}},
  Author                   = {Setia Pramana and Dan Lin and Philippe Haldermans and Ziv Shkedy and Tobias Verbeke and Hinrich G{\"o}hlmann and An De Bondt and Willem Talloen and Luc Bijnens.},
  Journal                  = {{The R Journal}},
  Year                     = {2010},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {5--12},
  Volume                   = {2},

  Url                      = {http://journal.r-project.org/archive/2010-1/RJournal_2010-1_Pramana~et~al.pdf}
}

@InCollection{RCT_2013,
  Title                    = {{R: A Language and Environment for Statistical Computing}},
  Author                   = {{R Core Team}},
  Year                     = {2013},

  Address                  = {Vienna, Austria},

  Organization             = {R Foundation for Statistical Computing},
  Url                      = {http://www.R-project.org/}
}

@Manual{RDCT2014c,
  Title                    = {{{R} Data {Import/Export}}},

  Address                  = {Vienna, Austria},
  Author                   = {{{R} Development Core Team}},
  Note                     = {{ISBN} 3-900051-10-0},
  Organization             = {{R} Foundation for Statistical Computing},
  Year                     = {2014},

  Url                      = {http://www.R-project.org/}
}

@Article{roediger_RKWard_2013,
  Title                    = {{Analysis of Data from Experimental {qPCR} Systems with {RKWard}}},
  Author                   = {Stefan R{\"o}diger and Alexander B{\"o}hm and J{\"o}rg Nitschke and Peter Schierack and Werner Lehmann and Ingolf Schimke and Christian Schr{\"o}der},
  Journal                  = {{qPCR} \& {NGS} 2013 Prodceedings; ISBN 9783000410246},
  Year                     = {2013},

  Month                    = mar,

  Abstract                 = {Introduction Real-time quantitative {PCR} ({qPCR)} is a well established method for the precise quantification of nucleic acids. However, there is an ongoing development of novel device concepts for {qPCRs}, hybridization and melting curve analysis. This includes lab-on-chip systems or the recently published microbead-based {VideoScan} platform [1]. Besides dedicated hardware an easy to use software for data analysis is also a requirement during developmental processes. Experimental instruments usually collect data of different formats and quality. Analysis software must therefore be able to preprocess data of different origins. Preprocessing includes data import, removal of missing values, selection of data ranges, outlier removal, detection of background signal, data transformation and smoothing /normalization of data. Subsequently, the software must be able to analyze and quantify both, the amplification and melting curve data. To automate this process we developed a set of plugins for the open source software {RKWard.} Novel technologies provide scientist with a high degree of freedom to adapt a system to specific requirements. {RKWard} provides all tools of R for an interactive data analysis and presentation, a dedicated output file and an output window for documenting results and figures. {RKWard} does not impose artificial limitations on how users can work with the application. For example, the user is not limited to using only one data.frame or one model at a time. The software aims to accelerate the development process and to enable easier collaboration between colleagues in the hardware and wetware disciplines. The plugins presented here are currently in beta stage and available upon request. In future the plugins will be provided for downloading as contributed ``plugin Packs".},
  Url                      = {http://www.gene-quantification.de/qpcr-ngs-2013/posters/P088-qPCR-NGS-2013.pdf},
  Urldate                  = {2014-04-08}
}

@Article{roediger_RJ_2013,
  Title                    = {{Surface Melting Curve Analysis with {R}}},
  Author                   = {Stefan R{\"o}diger and Alexander B{\"o}hm and Ingolf Schimke},
  Journal                  = {The R Journal},
  Year                     = {2013},

  Month                    = dec,
  Number                   = {2},
  Pages                    = {37--53},
  Volume                   = {5},

  Url                      = {http://journal.r-project.org/archive/2013-2/roediger-bohm-schimke.pdf}
}

@Article{rodiger_rkward_2012,
  Title                    = {{{RKWard:} A Comprehensive Graphical User Interface and Integrated Development Environment for Statistical Analysis with {R}}},
  Author                   = {Stefan R{\"o}diger and Thomas Friedrichsmeier and Prasenjit Kapat and Meik Michalke},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2012},
  Number                   = {9},
  Pages                    = {1--34},
  Volume                   = {49},

  ISSN                     = {1548-7660},
  Url                      = {http://www.jstatsoft.org/v49/i09}
}

@Article{rodiger_nucleic_2014,
  Title                    = {{Nucleic acid detection based on the use of microbeads: a review}},
  Author                   = {Stefan R{\"o}diger and Claudia Liebsch and Carsten Schmidt and Werner Lehmann and Ute Resch-Genger and Uwe Schedler and Peter Schierack},
  Journal                  = {Microchimica Acta},
  Year                     = {2014},

  Month                    = aug,
  Number                   = {11-12},
  Pages                    = {1151--1168},
  Volume                   = {181},

  Abstract                 = {Microbead-based technologies represent elegant and versatile approaches for highly parallelized quantitative multiparameter assays. They also form the basis of various techniques for detection and quantification of nucleic acids and proteins. Nucleic acid-based methods include hybridization assays, solid-phase {PCR}, sequencing, and trapping assays. Microbead assays have been improved in the past decades and are now important tools in routine and point-of-care diagnostics as well as in life science. Its advances include low costs, low workload, high speed and high-throughput automation. The potential of microbead-based assays therefore is apparent, and commercial applications can be found in the detection and discrimination of single nucleotide polymorphism, of pathogens, and in trapping assays. This review provides an overview on microbead-based platforms for biosensing with a main focus on nucleic acid detection (including amplification strategies and on selected probe systems using fluorescent labeling). Specific sections cover chemical properties of microbeads, the coupling of targets onto solid surfaces, microbead probe systems (mainly oligonucleotide probes), microbead detection schemes (with subsections on suspension arrays, microfluidic devices, and immobilized microbeads), quantification of nucleic acids, {PCR} in solution and the detection of amplicons, and methods for solid-phase amplification. We discuss selected trends such as microbead-coupled amplification, heterogeneous and homogenous {DNA} hybridization assays, real-time assays, melting curve analysis, and digital microbead assays. We finally discuss the relevance and trends of the methods in terms of high-level multiplexed analysis and their potential in diagnosis and personalized medicine. Contains 211 references.},
  Doi                      = {10.1007/s00604-014-1243-4},
  ISSN                     = {0026-3672, 1436-5073},
  Keywords                 = {Analytical Chemistry; Characterization and Evaluation of Materials; Microbead; Microbead array; Microengineering; Microfluidic; Multiplex; Nanochemistry; Nanotechnology; {PCR}; Real-time},
  Language                 = {en},
  Shorttitle               = {Nucleic acid detection based on the use of microbeads},
  Url                      = {http://link.springer.com/article/10.1007/s00604-014-1243-4},
  Urldate                  = {2014-09-23}
}

@Article{roediger_highly_2013,
  Title                    = {{A highly versatile microscope imaging technology platform for the multiplex real-time detection of biomolecules and autoimmune antibodies}},
  Author                   = {Stefan R{\"o}diger and Peter Schierack and Alexander B{\"o}hm and J{\"o}rg Nitschke and Ingo Berger and Ulrike Fr{\"o}mmel and Carsten Schmidt and Mirko Ruhland and Ingolf Schimke and Dirk Roggenbuck and Werner Lehmann and Christian Schr{\"o}der},
  Journal                  = {Advances in Biochemical Engineering/Biotechnology},
  Year                     = {2013},
  Pages                    = {35--74},
  Volume                   = {133},

  Abstract                 = {The analysis of different biomolecules is of prime importance for life science research and medical diagnostics. Due to the discovery of new molecules and new emerging bioanalytical problems, there is an ongoing demand for a technology platform that provides a broad range of assays with a user-friendly flexibility and rapid adaptability to new applications. Here we describe a highly versatile microscopy platform, {VideoScan}, for the rapid and simultaneous analysis of various assay formats based on fluorescence microscopic detection. The technological design is equally suitable for assays in solution, microbead-based assays and cell pattern recognition. The multiplex real-time capability for tracking of changes under dynamic heating conditions makes it a useful tool for {PCR} applications and nucleic acid hybridization, enabling kinetic data acquisition impossible to obtain by other technologies using endpoint detection. The paper discusses the technological principle of the platform regarding data acquisition and processing. Microbead-based and solution applications for the detection of diverse biomolecules, including antigens, antibodies, peptides, oligonucleotides and amplicons in small reaction volumes, are presented together with a high-content detection of autoimmune antibodies using a {HEp}-2 cell assay. Its adaptiveness and versatility gives {VideoScan} a competitive edge over other bioanalytical technologies.},
  ISSN                     = {0724-6145},
  Keywords                 = {Antibodies; Biological Assay; Computer Systems; Microscopy; Fluorescence; Microspheres; Nucleic Acid Hybridization; Pathology; Molecular; Polymerase chain reaction},
  Language                 = {eng},
  Pmid                     = {22437246}
}

@Article{roediger_bead_qPCR_2013,
  Title                    = {{{VideoScan} - A Microscope Imaging Technology Platform for the Multiplex Real-Time {PCR}}},
  Author                   = {Stefan R{\"o}diger and Peter Schierack and Alexander B{\"o}hm and J{\"o}rg Nitschke and Werner Lehmann and Christian Schr{\"o}der},
  Journal                  = {{qPCR} \& {NGS} 2013 Prodceedings; ISBN 9783000410246},
  Year                     = {2013},

  Month                    = mar,

  Abstract                 = {The quantitative real-time {PCR} ({qPCR)} is the central technology for the quantification of nucleic acids. The ability to perform multiplex quantitative real-time {PCRs} ({mqPCR)} in one vessel depends on parallel read-out and high sample multiplexing. Conventional {mqPCRs} are challenging due to the lack of multiple spectral non-overlapping reporter dyes and technical limitations of the detection systems. Recently we published a highly versatile microscopy platform, designated {VideoScan}, for the rapid and simultaneous analysis of various assay formats based on fluorescence microscopic detection. It uses standard commercial hardware components together with a modular in-house developed software package. Multiplex assays can be performed in solution or on solid-phases as microbead-based assays and cell assays using conventional consumables. The system has been proven to be applicable for multiplex {qPCRs}, nucleic acid hybridization assays and melting curve analysis [1]. As we proposed earlier [2] our microbead technology in combination with {qPCR} generates a powerful technology for multiplex quantitative {PCRs.} In the present work we demonstrate our {VideoScan} technology for microbead-based multiplex quantitative real-time {PCRs.} The assay employs the interaction between gene-specific capture probes bound to thermostable microbeads and mobile gene-specific degradable detection probes which report the amplification. Our system requires only two encoding dyes for encoding of multiple microbead populations and one reporter dye for microbead bound capture probes for the discrimination of at least twelve amplification reactions. We used the method for the quantitative detection of several human and bacterial genes. The assay is amplicon size independent, requires no alteration of the {PCR} product, employs a simple probe structure and has no background fluorescence. Early results indicate that the amplification efficiency is similar to reactions in solution. ----- [1] R{\"o}diger S, Schierack P, B{\"o}hm A, Nitschke J, Berger I, Fr{\"o}mmel U, Schmidt C, Ruhland M, Schimke I, Roggenbuck D, Lehmann W \& Schr{\"o}der C A Highly Versatile Microscope Imaging Technology Platform for the Multiplex Real-Time Detection of Biomolecules and Autoimmune Antibodies. Advances in Biochemical {Bioengineering/Biotechnology} (2012) [2] Lehmann W, B{\"o}hm A, Grossmann K, Hiemann R, Nitschke J \& R{\"o}diger S Method for carrying out and evaluating mix \& measure assays for the measurement of reaction kinetics, concentrations and affinities of analytes in multiplex format. (2008) Patent: Publication number: {US} 2010/0203572},
  Url                      = {http://www.gene-quantification.de/qpcr-ngs-2013/posters/P066-qPCR-NG S-2013.pdf},
  Urldate                  = {2014-04-08}
}

@Article{rao_2013,
  Title                    = {{A new method for quantitative real-time polymerase chain reaction data analysis}},
  Author                   = {Xiayu Rao and Dejian Lai and Xuelin Huang},
  Journal                  = {Journal of computational biology: a journal of computational molecular cell biology},
  Year                     = {2013},

  Month                    = sep,
  Note                     = {{PMID:} 23841653 {PMCID:} {PMC3762066}},
  Number                   = {9},
  Pages                    = {703--711},
  Volume                   = {20},

  Abstract                 = {Quantitative real-time polymerase chain reaction ({qPCR)} is a sensitive gene quantification method that has been extensively used in biological and biomedical fields. The currently used methods for {PCR} data analysis, including the threshold cycle method and linear and nonlinear model-fitting methods, all require subtracting background fluorescence. However, the removal of background fluorescence can hardly be accurate and therefore can distort results. We propose a new method, the taking-difference linear regression method, to overcome this limitation. Briefly, for each two consecutive {PCR} cycles, we subtract the fluorescence in the former cycle from that in the latter cycle, transforming the n cycle raw data into n-1 cycle data. Then, linear regression is applied to the natural logarithm of the transformed data. Finally, {PCR} amplification efficiencies and the initial {DNA} molecular numbers are calculated for each reaction. This taking-difference method avoids the error in subtracting an unknown background, and thus it is more accurate and reliable. This method is easy to perform, and this strategy can be extended to all current methods for {PCR} data analysis.},
  Doi                      = {10.1089/cmb.2012.0279},
  ISSN                     = {1557-8666},
  Keywords                 = {{DNA}; Fluorescence; Linear Models; Real-Time Polymerase Chain Reaction},
  Language                 = {eng}
}

@Article{Razali2011,
  Title                    = {{Power comparisons of Shapiro-Wilk, Kolmogorov-Smirnov, Lilliefors and Anderson-Darling tests}},
  Author                   = {Nornadiah Razali and Yap B. Wah},
  Journal                  = {{Journal of Statistical Modeling and Analytics}},
  Year                     = {2011},

  Month                    = jun,
  Number                   = {1},
  Volume                   = {2},

  Citeulike-article-id     = {12686206},
  Keywords                 = {statistics},
  Posted-at                = {2013-10-03 08:26:42},
  Priority                 = {2}
}

@Article{ritz_2008,
  Title                    = {{{qpcR:} an {R} package for sigmoidal model selection in quantitative real-time polymerase chain reaction analysis}},
  Author                   = {Christian Ritz and Andrej-Nikolai Spiess},
  Journal                  = {Bioinformatics},
  Year                     = {2008},

  Month                    = jul,
  Note                     = {{PMID:} 18482995},
  Number                   = {13},
  Pages                    = {1549--1551},
  Volume                   = {24},

  Abstract                 = {Summary: The {qpcR} library is an add-on to the free R statistical environment performing sigmoidal model selection in real-time quantitative polymerase chain reaction ({PCR)} data analysis. Additionally, the package implements the most commonly used algorithms for real-time {PCR} data analysis and is capable of extensive statistical comparison for the selection and evaluation of the different models based on several measures of goodness of fit. Availability: www.dr-{spiess.de/qpcR.html.} Contact: a.spiess@uke.uni-hamburg.de Supplementary Information: Statistical evaluations of the implemented methods can be found at www.dr-spiess.de under {{\lq}Supplemental} Data{\rq}.},
  Doi                      = {10.1093/bioinformatics/btn227},
  ISSN                     = {1367-4803, 1460-2059},
  Language                 = {en},
  Shorttitle               = {{qpcR}},
  Url                      = {http://bioinformatics.oxfordjournals.org/content/24/13/1549},
  Urldate                  = {2014-04-07}
}

@Article{Ritz_2005,
  Title                    = {{Bioassay Analysis using R}},
  Author                   = {C. Ritz and J. C. Streibig},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2005},
  Volume                   = {12},

  Issue                    = {5},
  Url                      = {http://www.bioassay.dk}
}

@Manual{roediger_chippcr_2014,
  Title                    = {{{chipPCR}: Toolkit of Helper Functions to Pre-Process Amplification Data}},
  Author                   = {Stefan Roediger and Michal Burdukiewicz},
  Month                    = jun,
  Year                     = {2014},

  Abstract                 = {The {chipPCR} package is a toolkit of functions to preprocess amplification curve data. Amplification data can be obtained from conventional {PCR} reactions or isothermal amplification reactions. The package contains functions to normalize and baseline amplification curves, a routine to detect the start of an amplification reaction, several smoothers for amplification data, a function to distinguish positive and negative amplification reactions and a function to determine the amplification efficiency. The smoothers are based on {LOWESS}, moving average, cubic splines, Savitzky-Golay and others. In addition the first approximate approximate derivative maximum ({FDM}) and second approximate derivative maximum ({SDM}) can be calculated by a 5-point-stencil as quantification points from real-time amplification curves. {chipPCR} contains data sets of experimental nucleic acid amplification systems including the {VideoScan} {HCU} and a capillary convective {PCR} ({ccPCR}) system. The amplification data were generated by helicase dependent amplification ({HDA}) or polymerase chain reaction ({PCR}) under various temperature conditions. As detection system intercalating dyes ({EvaGreen}, {SYBR} Green) and hydrolysis probes ({TaqMan}) were used. The latest source code is available via: https://github.com/michbur/{chipPCR}},
  Copyright                = {{GPL}-3},
  Shorttitle               = {{chipPCR}},
  Url                      = {http://cran.r-project.org/web/packages/chipPCR/index.html},
  Urldate                  = {2014-07-01}
}

@Manual{qualityTools,
  Title                    = {{qualityTools: Statistics in Quality Science.}},
  Author                   = {Thomas Roth},
  Note                     = {R package version 1.54 http://www.r-qualitytools.org},
  Year                     = {2012},

  Url                      = {http://www.r-qualitytools.org}
}

@Manual{shiny_2014,
  Title                    = {{shiny: Web Application Framework for R}},
  Author                   = {{RStudio} and {Inc.}},
  Note                     = {R package version 0.10.1},
  Year                     = {2014},

  Url                      = {http://CRAN.R-project.org/package=shiny}
}

@Article{ruijter_fluorescent-increase_2014,
  Title                    = {{Fluorescent-increase kinetics of different fluorescent reporters used for {qPCR} depend on monitoring chemistry, targeted sequence, type of {DNA} input and {PCR} efficiency}},
  Author                   = {Jan M. Ruijter and Peter Lorenz and Jari M. Tuomi and Michael Hecker and Maurice J. B. van den Hoff},
  Journal                  = {Microchimica Acta},
  Pages                    = {1--8},

  Abstract                 = {The analysis of quantitative {PCR} data usually does not take into account the fact that the increase in fluorescence depends on the monitoring chemistry, the input of ds-{DNA} or ss-{cDNA}, and the directionality of the targeting of probes or primers. The monitoring chemistries currently available can be categorized into six groups: (A) {DNA-binding} dyes; (B) hybridization probes; (C) hydrolysis probes; (D) {LUX} primers; (E) hairpin primers; and (F) the {QZyme} system. We have determined the kinetics of the increase in fluorescence for each of these groups with respect to the input of both ds-{DNA} and ss-{cDNA.} For the latter, we also evaluated {mRNA} and {cDNA} targeting probes or primers. This analysis revealed three situations. Hydrolysis probes and {LUX} primers, compared to {DNA-binding} dyes, do not require a correction of the observed quantification cycle. Hybridization probes and hairpin primers require a correction of −1 cycle (dubbed C-lag), while the {QZyme} system requires the C-lag correction and an efficiency-dependent C-shift correction. A {PCR} efficiency value can be derived from the relative increase in fluorescence in the exponential phase of the amplification curve for all monitoring chemistries. In case of hydrolysis probes, {LUX} primers and hairpin primers, however, this should be performed after cycle 12, and for the {QZyme} system after cycle 19, to keep the overestimation of the {PCR} efficiency below 0.5 \%. Figure The {qPCR} monitoring chemistries form six groups with distinct fluorescence kinetics. The displacement of the amplification curve depends on the chemistry, {DNA} input and probe-targeting. The observed shift in Cq values can be corrected and {PCR} efficiencies can be derived.},
  Doi                      = {10.1007/s00604-013-1155-8},
  ISSN                     = {0026-3672, 1436-5073},
  Keywords                 = {Analytical Chemistry; Characterization and Evaluation of Materials; {DNA-binding} dyes; Hybridization probes; Hydrolysis probes; Microengineering; Monitoring chemistry; Nanochemistry; Nanotechnology; {PCR} efficiency; Quantitative {PCR}},
  Language                 = {en},
  Url                      = {http://link.springer.com/article/10.1007/s00604-013-1155-8},
  Urldate                  = {2014-04-08}
}

@Article{ruijter_2014,
  Title                    = {{Fluorescent-increase kinetics of different fluorescent reporters used for {qPCR} depend on monitoring chemistry, targeted sequence, type of {DNA} input and {PCR} efficiency}},
  Author                   = {Jan M. Ruijter and Peter Lorenz and Jari M. Tuomi and Michael Hecker and Maurice J. B. van den Hoff},
  Journal                  = {Microchimica Acta},
  Year                     = {2014},
  Pages                    = {1--8},

  Abstract                 = {The analysis of quantitative {PCR} data usually does not take into account the fact that the increase in fluorescence depends on the monitoring chemistry, the input of ds-{DNA} or ss-{cDNA}, and the directionality of the targeting of probes or primers. The monitoring chemistries currently available can be categorized into six groups: (A) {DNA-binding} dyes; (B) hybridization probes; (C) hydrolysis probes; (D) {LUX} primers; (E) hairpin primers; and (F) the {QZyme} system. We have determined the kinetics of the increase in fluorescence for each of these groups with respect to the input of both ds-{DNA} and ss-{cDNA.} For the latter, we also evaluated {mRNA} and {cDNA} targeting probes or primers. This analysis revealed three situations. Hydrolysis probes and {LUX} primers, compared to {DNA-binding} dyes, do not require a correction of the observed quantification cycle. Hybridization probes and hairpin primers require a correction of −1 cycle (dubbed C-lag), while the {QZyme} system requires the C-lag correction and an efficiency-dependent C-shift correction. A {PCR} efficiency value can be derived from the relative increase in fluorescence in the exponential phase of the amplification curve for all monitoring chemistries. In case of hydrolysis probes, {LUX} primers and hairpin primers, however, this should be performed after cycle 12, and for the {QZyme} system after cycle 19, to keep the overestimation of the {PCR} efficiency below 0.5 \%. Figure The {qPCR} monitoring chemistries form six groups with distinct fluorescence kinetics. The displacement of the amplification curve depends on the chemistry, {DNA} input and probe-targeting. The observed shift in Cq values can be corrected and {PCR} efficiencies can be derived.},
  Doi                      = {10.1007/s00604-013-1155-8},
  ISSN                     = {0026-3672, 1436-5073},
  Keywords                 = {Analytical Chemistry; Characterization and Evaluation of Materials; {DNA-binding} dyes; Hybridization probes; Hydrolysis probes; Microengineering; Monitoring chemistry; Nanochemistry; Nanotechnology; {PCR} efficiency; Quantitative {PCR}},
  Language                 = {en},
  Url                      = {http://link.springer.com/article/10.1007/s00604-013-1155-8},
  Urldate                  = {2014-04-08}
}

@Article{ruijter_2013,
  Title                    = {{Evaluation of {qPCR} curve analysis methods for reliable biomarker discovery: bias, resolution, precision, and implications}},
  Author                   = {Jan M Ruijter and Michael W Pfaffl and Sheng Zhao and Andrej N Spiess and Gregory Boggy and Jochen Blom and Robert G Rutledge and Davide Sisti and Antoon Lievens and Katleen {De Preter} and Stefaan Derveaux and Jan Hellemans and Jo Vandesompele},
  Journal                  = {Methods (San Diego, Calif.)},
  Year                     = {2013},

  Month                    = jan,
  Note                     = {{PMID:} 22975077},
  Number                   = {1},
  Pages                    = {32--46},
  Volume                   = {59},

  Abstract                 = {{RNA} transcripts such as {mRNA} or {microRNA} are frequently used as biomarkers to determine disease state or response to therapy. Reverse transcription ({RT)} in combination with quantitative {PCR} ({qPCR)} has become the method of choice to quantify small amounts of such {RNA} molecules. In parallel with the democratization of {RT-qPCR} and its increasing use in biomedical research or biomarker discovery, we witnessed a growth in the number of gene expression data analysis methods. Most of these methods are based on the principle that the position of the amplification curve with respect to the cycle-axis is a measure for the initial target quantity: the later the curve, the lower the target quantity. However, most methods differ in the mathematical algorithms used to determine this position, as well as in the way the efficiency of the {PCR} reaction (the fold increase of product per cycle) is determined and applied in the calculations. Moreover, there is dispute about whether the {PCR} efficiency is constant or continuously decreasing. Together this has lead to the development of different methods to analyze amplification curves. In published comparisons of these methods, available algorithms were typically applied in a restricted or outdated way, which does not do them justice. Therefore, we aimed at development of a framework for robust and unbiased assessment of curve analysis performance whereby various publicly available curve analysis methods were thoroughly compared using a previously published large clinical data set (Vermeulen et al., 2009) [11]. The original developers of these methods applied their algorithms and are co-author on this study. We assessed the curve analysis methods' impact on transcriptional biomarker identification in terms of expression level, statistical significance, and patient-classification accuracy. The concentration series per gene, together with data sets from unpublished technical performance experiments, were analyzed in order to assess the algorithms' precision, bias, and resolution. While large differences exist between methods when considering the technical performance experiments, most methods perform relatively well on the biomarker data. The data and the analysis results per method are made available to serve as benchmark for further development and evaluation of {qPCR} curve analysis methods ({http://qPCRDataMethods.hfrc.nl).}},
  Doi                      = {10.1016/j.ymeth.2012.08.011},
  ISSN                     = {1095-9130},
  Keywords                 = {Area Under Curve; Bias (Epidemiology); Child; Gene Expression; Gene Expression Profiling; Humans; Kinetics; Neuroblastoma; Real-Time Polymerase Chain Reaction; Reference Standards; {ROC} Curve; Tumor Markers; Biological},
  Language                 = {eng},
  Shorttitle               = {Evaluation of {qPCR} curve analysis methods for reliable biomarker discovery}
}

@Article{ruijter_2009,
  Title                    = {{Amplification efficiency: linking baseline and bias in the analysis of quantitative {PCR} data}},
  Author                   = {J M Ruijter and C Ramakers and W M H Hoogaars and Y Karlen and O Bakker and M J B van den Hoff and A F M Moorman},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2009},

  Month                    = apr,
  Note                     = {{PMID:} 19237396 {PMCID:} {PMC2665230}},
  Number                   = {6},
  Pages                    = {e45},
  Volume                   = {37},

  Abstract                 = {Despite the central role of quantitative {PCR} ({qPCR)} in the quantification of {mRNA} transcripts, most analyses of {qPCR} data are still delegated to the software that comes with the {qPCR} apparatus. This is especially true for the handling of the fluorescence baseline. This article shows that baseline estimation errors are directly reflected in the observed {PCR} efficiency values and are thus propagated exponentially in the estimated starting concentrations as well as 'fold-difference' results. Because of the unknown origin and kinetics of the baseline fluorescence, the fluorescence values monitored in the initial cycles of the {PCR} reaction cannot be used to estimate a useful baseline value. An algorithm that estimates the baseline by reconstructing the log-linear phase downward from the early plateau phase of the {PCR} reaction was developed and shown to lead to very reproducible {PCR} efficiency values. {PCR} efficiency values were determined per sample by fitting a regression line to a subset of data points in the log-linear phase. The variability, as well as the bias, in {qPCR} results was significantly reduced when the mean of these {PCR} efficiencies per amplicon was used in the calculation of an estimate of the starting concentration per sample.},
  Doi                      = {10.1093/nar/gkp045},
  ISSN                     = {1362-4962},
  Keywords                 = {Algorithms; Animals; Chick Embryo; Fluorescence; Linear Models; Reverse Transcriptase Polymerase Chain Reaction},
  Language                 = {eng},
  Shorttitle               = {Amplification efficiency}
}

@Article{rutledge_2008,
  Title                    = {{A kinetic-based sigmoidal model for the polymerase chain reaction and its application to high-capacity absolute quantitative real-time {PCR}}},
  Author                   = {Robert G Rutledge and Don Stewart},
  Journal                  = {{BMC} Biotechnology},
  Year                     = {2008},
  Note                     = {{PMID:} 18466619 {PMCID:} {PMC2397388}},
  Pages                    = {47},
  Volume                   = {8},

  Abstract                 = {{BACKGROUND:} Based upon defining a common reference point, current real-time quantitative {PCR} technologies compare relative differences in amplification profile position. As such, absolute quantification requires construction of target-specific standard curves that are highly resource intensive and prone to introducing quantitative errors. Sigmoidal modeling using nonlinear regression has previously demonstrated that absolute quantification can be accomplished without standard curves; however, quantitative errors caused by distortions within the plateau phase have impeded effective implementation of this alternative approach. {RESULTS:} Recognition that amplification rate is linearly correlated to amplicon quantity led to the derivation of two sigmoid functions that allow target quantification via linear regression analysis. In addition to circumventing quantitative errors produced by plateau distortions, this approach allows the amplification efficiency within individual amplification reactions to be determined. Absolute quantification is accomplished by first converting individual fluorescence readings into target quantity expressed in fluorescence units, followed by conversion into the number of target molecules via optical calibration. Founded upon expressing reaction fluorescence in relation to amplicon {DNA} mass, a seminal element of this study was to implement optical calibration using lambda {gDNA} as a universal quantitative standard. Not only does this eliminate the need to prepare target-specific quantitative standards, it relegates establishment of quantitative scale to a single, highly defined entity. The quantitative competency of this approach was assessed by exploiting "limiting dilution assay" for absolute quantification, which provided an independent gold standard from which to verify quantitative accuracy. This yielded substantive corroborating evidence that absolute accuracies of +/- 25\% can be routinely achieved. Comparison with the {LinReg} and Miner automated {qPCR} data processing packages further demonstrated the superior performance of this kinetic-based methodology. {CONCLUSION:} Called "linear regression of efficiency" or {LRE}, this novel kinetic approach confers the ability to conduct high-capacity absolute quantification with unprecedented quality control capabilities. The computational simplicity and recursive nature of {LRE} quantification also makes it amenable to software implementation, as demonstrated by a prototypic Java program that automates data analysis. This in turn introduces the prospect of conducting absolute quantification with little additional effort beyond that required for the preparation of the amplification reactions.},
  Doi                      = {10.1186/1472-6750-8-47},
  ISSN                     = {1472-6750},
  Keywords                 = {Algorithms; Artifacts; Bacteriophage lambda; Calibration; {DNA}; Viral; Fluorescence; Gene Expression Profiling; Kinetics; Models; Chemical; Pattern Recognition; Automated; Polymerase chain reaction; Quality Control; Reference Standards; Reference Values; Regression Analysis; Reproducibility of Results; Research Design; Sequence Analysis; {DNA}; Software; Taq Polymerase; Weights and Measures},
  Language                 = {eng}
}

@Article{savitzky_1964,
  Title                    = {{Smoothing and Differentiation of Data by Simplified Least Squares Procedures.}},
  Author                   = {Abraham. Savitzky and M. J. E. Golay},
  Journal                  = {Analytical Chemistry},
  Year                     = {1964},

  Month                    = jul,
  Number                   = {8},
  Pages                    = {1627--1639},
  Volume                   = {36},

  Doi                      = {10.1021/ac60214a047},
  ISSN                     = {0003-2700},
  Url                      = {http://dx.doi.org/10.1021/ac60214a047},
  Urldate                  = {2014-02-20}
}

@Article{Schmidberger_2009,
  Title                    = {{State of the Art in Parallel Computing with {R}}},
  Author                   = {Markus Schmidberger and Martin Morgan and Dirk Eddelbuettel and Hao Yu and Luke Tierney and Ulrich Mansmann},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2009},

  Month                    = aug,
  Number                   = {1},
  Pages                    = {1--27},
  Volume                   = {31},

  Accepted                 = {2009-06-02},
  Bibdate                  = {2009-06-02},
  Coden                    = {JSSOBK},
  Day                      = {4},
  ISSN                     = {1548-7660},
  Submitted                = {2008-12-29},
  Url                      = {http://www.jstatsoft.org/v31/i01}
}

@Article{shain_2008,
  Title                    = {{A new method for robust quantitative and qualitative analysis of real-time {PCR}}},
  Author                   = {Eric B Shain and John M Clemens},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2008},

  Month                    = aug,
  Note                     = {{PMID:} 18603594 {PMCID:} {PMC2504305}},
  Number                   = {14},
  Pages                    = {e91},
  Volume                   = {36},

  Abstract                 = {An automated data analysis method for real-time {PCR} needs to exhibit robustness to the factors that routinely impact the measurement and analysis of real-time {PCR} data. Robust analysis is paramount to providing the same interpretation for results regardless of the skill of the operator performing or reviewing the work. We present a new method for analysis of real-time {PCR} data, the {maxRatio} method, which identifies a consistent point within or very near the exponential region of the {PCR} signal without requiring user intervention. Compared to other analytical techniques that generate only a cycle number, {maxRatio} generates several measurements of amplification including cycle numbers and relative measures of amplification efficiency and curve shape. By using these values, the {maxRatio} method can make highly reliable reactive/nonreactive determination along with quantitative evaluation. Application of the {maxRatio} method to the analysis of quantitative and qualitative real-time {PCR} assays is shown along with examples of method robustness to, and detection of, amplification response anomalies.},
  Doi                      = {10.1093/nar/gkn408},
  ISSN                     = {1362-4962},
  Keywords                 = {Chlamydia trachomatis; Female; {HIV-1}; Humans; Male; Neisseria gonorrhoeae; Polymerase chain reaction; {RNA}; Viral; Spectrometry; Fluorescence},
  Language                 = {eng}
}

@Manual{signal,
  Title                    = {{{signal}: Signal processing}},
  Author                   = {{signal developers}},
  Year                     = {2013},

  Url                      = {http://r-forge.r-project.org/projects/signal}
}

@Article{smith_2007,
  Title                    = {{Absolute estimation of initial concentrations of amplicon in a real-time {RT}-{PCR} process}},
  Author                   = {Marjo V. Smith and Chris R. Miller and Michael Kohn and Nigel J. Walker and Chris J. Portier},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2007},

  Month                    = oct,
  Number                   = {1},
  Pages                    = {409},
  Volume                   = {8},

  Abstract                 = {Since real time {PCR} was first developed, several approaches to estimating the initial quantity of template in an {RT}-{PCR} reaction have been tried. While initially only the early thermal cycles corresponding to exponential duplication were used, lately there has been an effort to use all of the cycles in a {PCR}. The efforts have included both fitting empirical sigmoid curves and more elaborate mechanistic models that explore the chemical reactions taking place during each cycle. The more elaborate mechanistic models require many more parameters than can be fit from a single amplification, while the empirical models provide little insight and are difficult to tailor to specific reactants. {PMID}: 17956631},
  Copyright                = {2007 Smith et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-8-409},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {17956631},
  Url                      = {http://www.biomedcentral.com/1471-2105/8/409/abstract},
  Urldate                  = {2014-07-01}
}

@Manual{Spiess_qpcR_2014,
  Title                    = {{qpcR: Modelling and analysis of real-time PCR data}},
  Author                   = {Andrej-Nikolai Spiess},
  Note                     = {R package version 1.4-0},
  Year                     = {2014}
}

@Article{spiess_impact_2014,
  Title                    = {{Impact of Smoothing on Parameter Estimation in Quantitative {DNA} Amplification Experiments}},
  Author                   = {Andrej-Nikolai Spiess and Claudia Deutschmann and Micha{\l} Burdukiewicz and Ralf Himmelreich and Katharina Klat and Peter Schierack and Stefan R{\"o}diger},
  Journal                  = {Clinical Chemistry},
  Year                     = {2014},

  Month                    = dec,
  Note                     = {00000},
  Pages                    = {clinchem.2014.230656},

  Abstract                 = {Background: Quantification cycle (Cq) and amplification efficiency ({AE}) are parameters mathematically extracted from raw data to characterize quantitative {PCR} ({qPCR}) reactions and quantify the copy number in a sample. Little attention has been paid to the effects of preprocessing and the use of smoothing or filtering approaches to compensate for noisy data. Existing algorithms largely are taken for granted, and it is unclear which of the various methods is most informative. We investigated the effect of smoothing and filtering algorithms on amplification curve data. Methods: We obtained published high-replicate {qPCR} datasets from standard block thermocyclers and other cycler platforms and statistically evaluated the impact of smoothing on Cq and {AE}. Results: Our results indicate that selected smoothing algorithms affect estimates of Cq and {AE} considerably. The commonly used moving average filter performed worst in all {qPCR} scenarios. The Savitzky--Golay smoother, cubic splines, and Whittaker smoother resulted overall in the least bias in our setting and exhibited low sensitivity to differences in {qPCR} {AE}, whereas other smoothers, such as running mean, introduced an {AE}-dependent bias. Conclusions: The selection of a smoothing algorithm is an important step in developing data analysis pipelines for real-time {PCR} experiments. We offer guidelines for selection of an appropriate smoothing algorithm in diagnostic {qPCR} applications. The findings of our study were implemented in the R packages {chipPCR} and {qpcR} as a basis for the implementation of an analytical strategy.},
  Doi                      = {10.1373/clinchem.2014.230656},
  ISSN                     = {0009-9147, 1530-8561},
  Language                 = {en},
  Url                      = {http://www.clinchem.org/content/early/2014/12/01/clinchem.2014.230656},
  Urldate                  = {2014-12-04}
}

@Article{spiess_2008,
  Title                    = {{Highly accurate sigmoidal fitting of real-time {PCR} data by introducing a parameter for asymmetry}},
  Author                   = {Andrej-Nikolai Spiess and Caroline Feig and Christian Ritz},
  Journal                  = {{BMC} Bioinformatics},
  Year                     = {2008},

  Month                    = apr,
  Number                   = {1},
  Pages                    = {221},
  Volume                   = {9},

  Abstract                 = {Fitting four-parameter sigmoidal models is one of the methods established in the analysis of quantitative real-time {PCR} ({qPCR}) data. We had observed that these models are not optimal in the fitting outcome due to the inherent constraint of symmetry around the point of inflection. Thus, we found it necessary to employ a mathematical algorithm that circumvents this problem and which utilizes an additional parameter for accommodating asymmetrical structures in sigmoidal {qPCR} data. {PMID}: 18445269},
  Copyright                = {2008 Spiess et al; licensee {BioMed} Central Ltd.},
  Doi                      = {10.1186/1471-2105-9-221},
  ISSN                     = {1471-2105},
  Language                 = {en},
  Pmid                     = {18445269},
  Url                      = {http://www.biomedcentral.com/1471-2105/9/221/abstract},
  Urldate                  = {2014-07-01}
}

@Article{stahlberg_2003,
  Title                    = {{Quantitative real-time {PCR} method for detection of B-lymphocyte monoclonality by comparison of kappa and lambda immunoglobulin light chain expression}},
  Author                   = {Anders St{\aa}{\aa}lberg and Pierre Aman and B{\"o}rje Ridell and Petter Mostad and Mikael Kubista},
  Journal                  = {Clinical {C}hemistry},
  Year                     = {2003},

  Month                    = jan,
  Note                     = {{PMID:} 12507960},
  Number                   = {1},
  Pages                    = {51--59},
  Volume                   = {49},

  Abstract                 = {{BACKGROUND:} An abnormal {IgLkappa:IgLlambda} ratio has long been used as a clinical criterion for non-Hodgkin B-cell lymphomas. As a first step toward a quantitative real-time {PCR-based} multimarker diagnostic analysis of lymphomas, we have developed a method for determination of {IgLkappa:IgLlambda} ratio in clinical samples. {METHODS:} Light-up probe-based real-time {PCR} was used to quantify {IgLkappa} and {IgLlambda} {cDNA} from 32 clinical samples. The samples were also investigated by routine immunohistochemical analysis and flow cytometry. {RESULTS:} Of 32 suspected non-Hodgkin lymphoma samples analyzed, 28 were correctly assigned from real-time {PCR} measurements assuming invariant {PCR} efficiencies in the biological samples. Four samples were false negatives. One was a T-cell lymphoma, one was a diffuse large B-cell lymphoma, and one was reanalyzed and found lymphoma-positive by in situ calibration, which takes into account sample-specific {PCR} inhibition. Twelve of the samples were fine-needle aspirates, and these were all correctly assigned. {CONCLUSIONS:} This work is a first step toward analyzing clinical samples by quantitative light-up probe-based real-time {PCR.} Quantitative real-time {PCR} appears suitable for high-throughput testing of cancers by measuring expression of tumor markers in fine-needle aspirates.},
  ISSN                     = {0009-9147},
  Keywords                 = {Algorithms; B-Lymphocytes; Humans; Immunoglobulin kappa-Chains; Immunoglobulin lambda-Chains; Lymph Nodes; Lymphoma; B-Cell; Models; Biological; Polymerase chain reaction},
  Language                 = {eng}
}

@Article{Stodden_2014,
  Title                    = {Best Practices for Computational Science: Software Infrastructure and Environments for Reproducible and Extensible Research},
  Author                   = {Victoria Stodden and Sheila Miguez},
  Journal                  = {Journal of Open Research Software},
  Year                     = {2014},
  Number                   = {1},
  Volume                   = {2},

  Abstract                 = {The goal of this article is to coalesce a discussion around best practices for scholarly research that utilizes computational methods, by providing a formalized set of best practice recommendations to guide computational scientists and other stakeholders wishing to disseminate reproducible research, facilitate innovation by enabling data and code re-use, and enable broader communication of the output of computational scientific research. Scholarly dissemination and communication standards are changing to reflect the increasingly computational nature of scholarly research, primarily to include the sharing of the data and code associated with published results. We also present these Best Practices as a living, evolving, and changing document at http://wiki.stodden.net/Best_Practices.},
  ISSN                     = {2049-9647},
  Keywords                 = {best practices; reproducible research; archiving; data sharing; code sharing; wiki; open science; computational science; scientific method},
  Url                      = {http://openresearchsoftware.metajnl.com/article/view/jors.ay}
}

@Manual{RDCT2014a,
  Title                    = {{Writing {R} Extensions}},

  Address                  = {Vienna, Austria},
  Author                   = {{R} Development Core Team and R Development Core Team},
  Note                     = {{ISBN} 3-900051-11-9},
  Organization             = {{R} Foundation for Statistical Computing},
  Year                     = {2014}
}

@Article{tellinghuisen_2014,
  Title                    = {{Comparing real-time quantitative polymerase chain reaction analysis methods for precision, linearity, and accuracy of estimating amplification efficiency}},
  Author                   = {Joel Tellinghuisen and Andrej-Nikolai Spiess},
  Journal                  = {Analytical {B}iochemistry},
  Year                     = {2014},

  Month                    = mar,
  Note                     = {{PMID:} 24365068},
  Pages                    = {76--82},
  Volume                   = {449},

  Abstract                 = {New methods are used to compare seven {qPCR} analysis methods for their performance in estimating the quantification cycle (Cq) and amplification efficiency (E) for a large test data set (94 samples for each of 4 dilutions) from a recent study. Precision and linearity are assessed using chi-square (χ(2)), which is the minimized quantity in least-squares ({LS)} fitting, equivalent to the variance in unweighted {LS}, and commonly used to define statistical efficiency. All methods yield Cqs that vary strongly in precision with the starting concentration N0, requiring weighted {LS} for proper calibration fitting of Cq vs log(N0). Then χ(2) for cubic calibration fits compares the inherent precision of the Cqs, while increases in χ(2) for quadratic and linear fits show the significance of nonlinearity. Nonlinearity is further manifested in unphysical estimates of E from the same Cq data, results which also challenge a tenet of all {qPCR} analysis methods - that E is constant throughout the baseline region. Constant-threshold (Ct) methods underperform the other methods when the data vary considerably in scale, as these data do.},
  Doi                      = {10.1016/j.ab.2013.12.020},
  ISSN                     = {1096-0309},
  Language                 = {eng}
}

@Article{thanakiatkrai_2012,
  Title                    = {Using the Taguchi method for rapid quantitative {PCR} optimization with {SYBR} Green I},
  Author                   = {Thanakiatkrai, Phuvadol and Welch, Lindsey},
  Journal                  = {International Journal of Legal Medicine},
  Year                     = {2012},

  Month                    = jan,
  Note                     = {00007 },
  Number                   = {1},
  Pages                    = {161--165},
  Volume                   = {126},

  Abstract                 = {Here, we applied the Taguchi method, an engineering optimization process, to successfully determine the optimal conditions for three {SYBR} Green I-based quantitative {PCR} assays. This method balanced the effects of all factors and their associated levels by using an orthogonal array rather than a factorial array. Instead of running 27 experiments with the conventional factorial method, the Taguchi method achieved the same optimal conditions using only nine experiments, saving valuable resources.},
  Doi                      = {10.1007/s00414-011-0558-5},
  ISSN                     = {1437-1596},
  Keywords                 = {Fluorescent Dyes, Forensic Sciences, Organic Chemicals, Real-Time Polymerase Chain Reaction},
  Language                 = {eng},
  Pmid                     = {21336638}
}

@Article{Thioulouse_2010,
  Title                    = {{Online Reproducible Research: An Application to Multivariate Analysis of Bacterial DNA Fingerprint Data}},
  Author                   = {Jean Thioulouse and Claire Valiente-Moro and Lionel Zenner},
  Journal                  = {{The R Journal}},
  Year                     = {2010},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {44--52},
  Volume                   = {2},

  Url                      = {http://journal.r-project.org/archive/2010-1/RJournal_2010-1_Thioulouse~et~al.pdf}
}

@Article{tichopad_2003,
  Title                    = {{Standardized determination of real-time {PCR} efficiency from a single reaction set-up}},
  Author                   = {Ales Tichopad and Michael Dilger and Gerhard Schwarz and Michael W Pfaffl},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2003},

  Month                    = oct,
  Note                     = {{PMID:} 14530455 {PMCID:} {PMC219490}},
  Number                   = {20},
  Pages                    = {e122},
  Volume                   = {31},

  Abstract                 = {We propose a computing method for the estimation of real-time {PCR} amplification efficiency. It is based on a statistic delimitation of the beginning of exponentially behaving observations in real-time {PCR} kinetics. {PCR} ground fluorescence phase, non-exponential and plateau phase were excluded from the calculation process by separate mathematical algorithms. We validated the method on experimental data on multiple targets obtained on the {LightCycler} platform. The developed method yields results of higher accuracy than the currently used method of serial dilutions for amplification efficiency estimation. The single reaction set-up estimation is sensitive to differences in starting concentrations of the target sequence in samples. Furthermore, it resists the subjective influence of researchers, and the estimation can therefore be fully instrumentalized.},
  ISSN                     = {1362-4962},
  Keywords                 = {Algorithms; Animals; Cattle; {DNA}; Fluorescence; Genes; sry; Organic Chemicals; Plasmids; Polymerase chain reaction; Reference Standards; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng}
}

@Article{tichopad_standardized_2003,
  Title                    = {{Standardized determination of real-time {PCR} efficiency from a single reaction set-up}},
  Author                   = {Ales Tichopad and Michael Dilger and Gerhard Schwarz and Michael W Pfaffl},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2003},

  Month                    = oct,
  Note                     = {{PMID:} 14530455 {PMCID:} {PMC219490}},
  Number                   = {20},
  Pages                    = {e122},
  Volume                   = {31},

  Abstract                 = {We propose a computing method for the estimation of real-time {PCR} amplification efficiency. It is based on a statistic delimitation of the beginning of exponentially behaving observations in real-time {PCR} kinetics. {PCR} ground fluorescence phase, non-exponential and plateau phase were excluded from the calculation process by separate mathematical algorithms. We validated the method on experimental data on multiple targets obtained on the {LightCycler} platform. The developed method yields results of higher accuracy than the currently used method of serial dilutions for amplification efficiency estimation. The single reaction set-up estimation is sensitive to differences in starting concentrations of the target sequence in samples. Furthermore, it resists the subjective influence of researchers, and the estimation can therefore be fully instrumentalized.},
  ISSN                     = {1362-4962},
  Keywords                 = {Algorithms; Animals; Cattle; {DNA}; Fluorescence; Genes; sry; Organic Chemicals; Plasmids; Polymerase chain reaction; Reference Standards; Reproducibility of Results; Sensitivity and Specificity},
  Language                 = {eng}
}

@Article{Todorov_2009,
  Title                    = {{An Object-Oriented Framework for Robust Multivariate Analysis}},
  Author                   = {Valentin Todorov and Peter Filzmoser},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2009},

  Month                    = oct,
  Number                   = {3},
  Pages                    = {1--47},
  Volume                   = {32},

  Accepted                 = {2009-08-08},
  Bibdate                  = {2009-08-08},
  Coden                    = {JSSOBK},
  Day                      = {14},
  ISSN                     = {1548-7660},
  Submitted                = {2009-02-26},
  Url                      = {http://www.jstatsoft.org/v32/i03}
}

@Article{tuomi_2010,
  Title                    = {{Bias in the Cq value observed with hydrolysis probe based quantitative {PCR} can be corrected with the estimated {PCR} efficiency value}},
  Author                   = {Jari Michael Tuomi and Frans Voorbraak and Douglas L Jones and Jan M Ruijter},
  Journal                  = {Methods (San Diego, Calif.)},
  Year                     = {2010},

  Month                    = apr,
  Note                     = {{PMID:} 20138998},
  Number                   = {4},
  Pages                    = {313--322},
  Volume                   = {50},

  Abstract                 = {For real-time monitoring of {PCR} amplification of {DNA}, quantitative {PCR} ({qPCR)} assays use various fluorescent reporters. {DNA} binding molecules and hybridization reporters (primers and probes) only fluoresce when bound to {DNA} and result in the non-cumulative increase in observed fluorescence. Hydrolysis reporters ({TaqMan} probes and {QZyme} primers) become fluorescent during {DNA} elongation and the released fluorophore remains fluorescent during further cycles; this results in a cumulative increase in observed fluorescence. Although the quantification threshold is reached at a lower number of cycles when fluorescence accumulates, in {qPCR} analysis no distinction is made between the two types of data sets. Mathematical modeling shows that ignoring the cumulative nature of the data leaves the estimated {PCR} efficiency practically unaffected but will lead to at least one cycle underestimation of the quantification cycle (C(q) value), corresponding to a 2-fold overestimation of target quantity. The effect on the target-reference ratio depends on the {PCR} efficiency of the target and reference amplicons. The leftward shift of the C(q) value is dependent on the {PCR} efficiency and with sufficiently large C(q) values, this shift is constant. This allows the C(q) to be corrected and unbiased target quantities to be obtained.},
  Doi                      = {10.1016/j.ymeth.2010.02.003},
  ISSN                     = {1095-9130},
  Keywords                 = {Animals; Bias (Epidemiology); Fluorescence; Fluorescent Dyes; Gene Expression; Heart Atria; Hydrolysis; Mice; Molecular Probes; Organic Chemicals; Polymerase chain reaction; {RNA}},
  Language                 = {eng}
}

@Article{Tusell_2010,
  Title                    = {{Kalman Filtering in R}},
  Author                   = {Fernando Tusell},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2011},

  Month                    = mar,
  Number                   = {2},
  Pages                    = {1--27},
  Volume                   = {39},

  Accepted                 = {2010-08-17},
  Bibdate                  = {2010-08-17},
  Coden                    = {JSSOBK},
  Day                      = {1},
  ISSN                     = {1548-7660},
  Submitted                = {2010-01-12},
  Url                      = {http://www.jstatsoft.org/v39/i02}
}

@Article{Valero_2012,
  Title                    = {{Graphical User Interfaces for {R}}},
  Author                   = {Pedro M. Valero-Mora and Ruben Ledesma},
  Journal                  = {Journal of Statistical Software},
  Year                     = {2012},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {1--8},
  Volume                   = {49},

  Accepted                 = {2012-06-03},
  Bibdate                  = {2012-06-03},
  Coden                    = {JSSOBK},
  Day                      = {30},
  ISSN                     = {1548-7660},
  Submitted                = {2012-06-03},
  Url                      = {http://www.jstatsoft.org/v49/i01}
}

@Article{waggott_2012,
  Title                    = {{{NanoStringNorm}: An Extensible {R} Package For the Pre-Processing of {NanoString} {mRNA} and {miRNA} Data}},
  Author                   = {Daryl Waggott and Kenneth Chu and Shaoming Yin and Bradly G. Wouters and Fei-Fei Liu and Paul C. Boutros},
  Journal                  = {Bioinformatics},
  Year                     = {2012},

  Month                    = apr,
  Pages                    = {bts188},

  Abstract                 = {Motivation: The {NanoString} {nCounter} Platform is a new and promising technology for measuring nucleic acid abundances. It has several advantages over {PCR}-based techniques, including avoidance of amplification, direct sequence interrogation and digital detection for absolute quantification. These features minimize aspects of experimental error that hold promise for dealing with challenging experimental conditions, such as archival Formalin-Fixed Paraffin-Embedded ({FFPE}) samples. However, systematic inter-sample technical artifacts caused by variability in sample preservation, bio-molecular extraction, and platform fluctuations must be removed to ensure robust data. Results: To facilitate this process and to address these issues for {NanoString} datasets, we have written a pre-processing package called {NanoStringNorm} in the R statistical language. Key features include an extensible environment for method comparison and new algorithm development, integrated gene and sample diagnostics, and facilitated downstream statistical analysis. The package is open-source, is available through the {CRAN} package repository, includes unit-tests to ensure numerical accuracy, and provides visual and numeric diagnostics. Availability: http://cran.r-project.org/web/packages/{NanoStringNorm} Contact: Paul.Boutros@oicr.on.ca Supplementary: Supplementary data available at Bioinformatics online.},
  Doi                      = {10.1093/bioinformatics/bts188},
  ISSN                     = {1367-4803, 1460-2059},
  Language                 = {en},
  Pmid                     = {22513995},
  Shorttitle               = {{NanoStringNorm}},
  Url                      = {http://bioinformatics.oxfordjournals.org/content/early/2012/04/15/bioinformatics.bts188},
  Urldate                  = {2014-09-20}
}

@Article{wilhelm_2003,
  Title                    = {{Real-time {PCR-based} method for the estimation of genome sizes}},
  Author                   = {Jochen Wilhelm and Alfred Pingoud and Meinhard Hahn},
  Journal                  = {Nucleic {A}cids {R}esearch},
  Year                     = {2003},

  Month                    = may,
  Note                     = {{PMID:} 12736322 {PMCID:} {PMC156059}},
  Number                   = {10},
  Pages                    = {e56},
  Volume                   = {31},

  Abstract                 = {The fast and reliable estimation of the genome sizes of various species would allow for a systematic analysis of many organisms and could reveal insights into evolutionary processes. Many methods for the estimation of genome sizes have already been described. The classical methods are based on the determination of the phosphate content in the {DNA} backbone of total {DNA} isolated from a defined number of cells or on reassociation kinetics of high molecular weight genomic {DNA} (c0t assay). More recent techniques employ {DNA-specific} fluorescent dyes in flow cytometry analysis, image analysis or absorption cytometry after Feulgen staining. The method presented here is based on the absolute quantification of genetic elements in a known amount (mass) of genomic {DNA} by real-time quantitative {PCR.} The method was evaluated on three different eukaryotic species, Saccharomyces cerevisiae (12.1 Mb), Xiphophorus maculatus (550 Mb) and Homo sapiens sapiens (2.9 Gb), and found to be fast, highly accurate and reliable.},
  ISSN                     = {0305-1048},
  Url                      = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC156059/},
  Urldate                  = {2014-04-27}
}

@Article{Guazzelli_2009,
  Title                    = {{PMML: An Open Standard for Sharing Models}},
  Author                   = {Michael Zeller and Wen-Ching Lin Alex Guazzelli and Graham Williams},
  Journal                  = {The R Journal},
  Year                     = {2009},

  Month                    = jun,
  Number                   = {1},
  Pages                    = {60--65},
  Volume                   = {1},

  Abstract                 = {The PMML package exports a variety of predic- tive and descriptive models from R to the Predic- tive Model Markup Language (Data Mining Group, 2008). PMML is an XML-based language and has become the de-facto standard to represent not only predictive and descriptive models, but also data pre- and post-processing. In so doing, it allows for the interchange of models among different tools and en- vironments, mostly avoiding proprietary issues and incompatibilities. The PMML package itself (Williams et al., 2009) was conceived at first as part of Togaware{\rq}s data min- ing toolkit Rattle, the R Analytical Tool To Learn Eas- ily (Williams, 2009). Although it can easily be ac- cessed through Rattle{\rq}s GUI, it has been separated from Rattle so that it can also be accessed directly in R. In the next section, we describe PMML and its overall structure. This is followed by a description of the functionality supported by the PMML pack- age and how this can be used in R. We then discuss the importance of working with a valid PMML file and finish by highlighting some of the debate sur- rounding the adoption of PMML by the data mining community at large.},
  Url                      = {http://journal.r-project.org/archive/2009-1/RJournal_2009-1_Guazzelli+et+al.pdf}
}

@Article{zhang_2007,
  Title                    = {{Miniaturized {PCR} chips for nucleic acid amplification and analysis: latest advances and future trends}},
  Author                   = {Chunsun Zhang and Da Xing},
  Journal                  = {Nucleic Acids {R}esearch},
  Year                     = {2007},

  Month                    = jul,
  Note                     = {{PMID:} 17576684 {PMCID:} {PMC1934988}},
  Number                   = {13},
  Pages                    = {4223--4237},
  Volume                   = {35},

  Abstract                 = {The possibility of performing fast and small-volume nucleic acid amplification and analysis on a single chip has attracted great interest. Devices based on this idea, referred to as micro total analysis, microfluidic analysis, or simply {{\lq}Lab} on a chip{\rq} systems, have witnessed steady advances over the last several years. Here, we summarize recent research on chip substrates, surface treatments, {PCR} reaction volume and speed, architecture, approaches to eliminating cross-contamination and control and measurement of temperature and liquid flow. We also discuss product-detection methods, integration of functional components, biological samples used in {PCR} chips, potential applications and other practical issues related to implementation of lab-on-a-chip technologies.},
  Doi                      = {10.1093/nar/gkm389},
  File                     = {PubMed Central Full Text PDF:/home/tux/.mozilla/firefox/kibijmoe.default/zotero/storage/W5MXCGKD/Zhang und Xing - 2007 - Miniaturized PCR chips for nucleic acid amplificat.pdf:application/pdf},
  ISSN                     = {0305-1048},
  Shorttitle               = {Miniaturized {PCR} chips for nucleic acid amplification and analysis},
  Url                      = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1934988},
  Urldate                  = {2014-05-02}
}

@Manual{Zhang_2010,
  Title                    = {{{ddCt}: The {ddCt} Algorithm for the Analysis of Quantitative Real-Time {PCR (qRT-PCR)}}},
  Author                   = {Jitao David Zhang and Rudolf Biczok and Markus Ruschhaupt},
  Year                     = {2010},

  Abstract                 = {The Delta-Delta-Ct (ddCt) Algorithm is an approximation method to determine relative gene expression with quantitative real-time PCR (qRT-PCR) experiments. Compared to other approaches, it requires no standard curve for each primer-target pair, therefore reducing the working load and yet returning accurate enough results as long as the assumptions of the amplification efficiency hold. The ddCt package implements a pipeline to collect, analyse and visualize qRT-PCR results, for example those from TaqMan SDM software, mainly using the ddCt method. The pipeline can be either invoked by a script in command-line or through the API consisting of S4-Classes, methods and functions.},
  Copyright                = {{LGPL}-3},
  Shorttitle               = {{ddCt}},
  Url                      = {http://www.bioconductor.org/packages/release/bioc/html/ddCt.html},
  Urldate                  = {2014-09-17}
}

@Article{zhang_2013,
  Title                    = {{{displayHTS}: a R package for displaying data and results from high-throughput screening experiments}},
  Author                   = {Xiaohua Douglas Zhang and Zhaozhi Zhang},
  Journal                  = {Bioinformatics},
  Year                     = {2013},

  Month                    = mar,
  Number                   = {6},
  Pages                    = {794--796},
  Volume                   = {29},

  Abstract                 = {Summary: The R package {displayHTS} implements recently developed methods and figures for displaying data and hit selection results in high-throughput screening ({HTS}) experiments. It generates not only certain useful distinctive graphics such as the plate-well series plot, plate image and dual-flashlight plot but also other commonly used figures such as volcano plot and plate correlation plot. These figures are critical for visualizing the data and displaying important features of {HTS} data and hit selection results. Availability and implementation: The package is freely available from {CRAN}: http://cran.r-project.org/mirrors.html, being distributed under the {GNU} General Public License. Contact: xiaohua\_zhang@merck.com Supplementary information: Supplementary data are available at Bioinformatics online.},
  Doi                      = {10.1093/bioinformatics/btt060},
  ISSN                     = {1367-4803, 1460-2059},
  Language                 = {en},
  Pmid                     = {23396118},
  Shorttitle               = {{displayHTS}},
  Url                      = {http://bioinformatics.oxfordjournals.org/content/29/6/794},
  Urldate                  = {2014-09-20}
}

@Article{zhao_2005,
  Title                    = {{Comprehensive algorithm for quantitative real-time polymerase chain reaction}},
  Author                   = {Sheng Zhao and Russell D Fernald},
  Journal                  = {Journal of computational biology: a journal of computational molecular cell biology},
  Year                     = {2005},

  Month                    = oct,
  Note                     = {{PMID:} 16241897 {PMCID:} {PMC2716216}},
  Number                   = {8},
  Pages                    = {1047--1064},
  Volume                   = {12},

  Abstract                 = {Quantitative real-time polymerase chain reactions ({qRT-PCR)} have become the method of choice for rapid, sensitive, quantitative comparison of {RNA} transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of {mRNA} levels. Calculating accurate {mRNA} levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold ({CT)} to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying {qRT-PCR} results using calculations based on the kinetics of individual {PCR} reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and {CT.} We use a four-parameter logistic model to fit the raw fluorescence data as a function of {PCR} cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For {CT} determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of {qRT-PCR} results that is independent of the specific equipment used to perform {PCR} reactions.},
  Doi                      = {10.1089/cmb.2005.12.1047},
  ISSN                     = {1066-5277},
  Keywords                 = {Algorithms; Animals; Cichlids; Linear Models; Logistic Models; Reproducibility of Results; Reverse Transcriptase Polymerase Chain Reaction; Software},
  Language                 = {eng}
}

@Article{zhao_comprehensive_2005,
  Title                    = {{Comprehensive Algorithm for Quantitative Real-Time Polymerase Chain Reaction}},
  Author                   = {Sheng Zhao and Russell D. Fernald},
  Journal                  = {Journal of {Computational Biology} : a {Journal} of {Computational Molecular Cell Biology}},
  Year                     = {2005},

  Month                    = oct,
  Note                     = {{PMID:} 16241897 {PMCID:} {PMC2716216}},
  Number                   = {8},
  Pages                    = {1047--1064},
  Volume                   = {12},

  Abstract                 = {Quantitative real-time polymerase chain reactions ({qRT-PCR)} have become the method of choice for rapid, sensitive, quantitative comparison of {RNA} transcript abundance. Useful data from this method depend on fitting data to theoretical curves that allow computation of {mRNA} levels. Calculating accurate {mRNA} levels requires important parameters such as reaction efficiency and the fractional cycle number at threshold ({CT)} to be used; however, many algorithms currently in use estimate these important parameters. Here we describe an objective method for quantifying {qRT-PCR} results using calculations based on the kinetics of individual {PCR} reactions without the need of the standard curve, independent of any assumptions or subjective judgments which allow direct calculation of efficiency and {CT.} We use a four-parameter logistic model to fit the raw fluorescence data as a function of {PCR} cycles to identify the exponential phase of the reaction. Next, we use a three-parameter simple exponent model to fit the exponential phase using an iterative nonlinear regression algorithm. Within the exponential portion of the curve, our technique automatically identifies candidate regression values using the P-value of regression and then uses a weighted average to compute a final efficiency for quantification. For {CT} determination, we chose the first positive second derivative maximum from the logistic model. This algorithm provides an objective and noise-resistant method for quantification of {qRT-PCR} results that is independent of the specific equipment used to perform {PCR} reactions.},
  Doi                      = {10.1089/cmb.2005.12.1047},
  ISSN                     = {1066-5277},
  Url                      = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2716216},
  Urldate                  = {2014-04-27}
}

